Artificial intelligence all

Episodic (vs. Sequential)

- Agent's experience can be divided into episodes, each episode with what an agent perceive and what is the action • Next episode does not depend on the previous episode - Current decision will affect all future sates in sequential environment

A variation of depth-first search that uses a depth limit

- Alleviates the problem of unbounded trees - Search to a predetermined depth l ("ell") - Nodes at depth l have no successors

search algorithm of artificial intelligence

A search algorithm takes a problem as input and returns a solution in the form of an action sequence

Single Agent Vs multi agent:

An agent solving a cross word puzzle by itself is clearly in a single agent environment. An agent playing chess is in a two agent environment.

Pragmatics concerns the overall communicative and social context and its effect on interpretation

- The ham sandwich wants another beer. (co-reference, anaphora) - John thinks vanilla. (ellipsis)

he Turing test is only one particular form of the Weak AI claim

: that an intelligent system could be built that can communicate as if it were a human, and the test itself has many drawbacks

Solutions

A Solution to the problem is the path from the initial state to the final state • Quality of solution is measured by path cost function - Optimal Solution has the lowest path cost among other solutions - An Agent with several immediate options of unknown value can decide what to do by first examining different possible sequences of actions that lead to a state of known value, and then choosing the best sequence Searching Process

Define Validity of a sentence

A sentence is valid or necessarily true if and only if it is true under all possible interpretation in all posssible world.

Validity and satisfiability

A sentence is valid or necessarily true if and only if it is true under all possible interpretations in all possible worlds

Define a Proof

A sequence of application of inference rules is called a proof. Finding proof is exactly finding solution to search problems. If the successor function is defined to generate all possible applications of inference rules then the search algorithms can be applied to find proof

search tree

A structure that represents alternatives in adversarial situations, such as game playing

Define terms.

A term is a logical expression that refers to an object. We use 3 symbols to build a term

Natural Languages vs. Computer Languages

Ambiguity is the primary difference between natural and computer languages. • Formal programming languages are designed to be unambiguous, i.e. they can be defined by a grammar that produces a unique parse for each sentence in the language.

Simple reflex agents: deciding on the action to take based only on the current perception and not on the history of perceptions.

Based on the condition-action rule: (if (condition) action) Works if the environment is fully observable

Function symbols:

Cosine. FatherOf, LeftLegOf

Model-Based Reflex Agents

Definition: An agent which combines the current percept with the old internal state to generate updated description of the current state

The advantages of this declarative approach

o system building have not been lost on other subfields of computer science

Define Information Gathering.

Doing actions in order to modify future percepts sometimes called information gathering.

Dualism holds

Dualism holds that the body and mind (or soul) are two different things, with the mind or soul existing outside of the physical plan

Ambiguity between literal and figurative meanings.

For example, the phrase Wall Street gains record profits does not imply that a physical street is making any money.

first-order logic (like natural language) assumes the world contains

Functions: father of, best friend, one more than, plus, • (relations in which there is only one value for a given inpu

Modes Tollens

If P then Q Not Q Therefore not P

Define iterative deepening search.

Iterative deepening is a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits: first depth 0, then depth 1,then depth 2& so on

Define a knowledge Base

Knowledge base is the central component of knowledge base agent and it is described as a set of representations of facts about the world

Define Logic

Logic is one which consist of i. A formal system for describing states of affairs, consisting of a) Syntax b)Semantics. ii. Proof Theory - a set of rules for deducing the entailment of a set sentences

natural langauge

NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.

Properties of Depth-First Search

No: fails in infinite-depth spaces, spaces with loops • Modify to avoid repeated spaces along path - Yes: in finite spaces • Time - O(bm ) - Not great if m is much larger than d - But if the solutions are dense, this may be faster than breadth-first search • Space - O(bm)...linear space • Optimal - No • When search hits a dead-end, can only back up one level at a time even if the "problem" occurs because of a bad operator choice near the top of the tree. Hence, only does "chronological backtracking"

classification in supervised learning

One of the major challenges of classification is generalisation. Given a labelled set of examples, the task of the learner is to be able to generalise to input examples it has not yet seen.

We may use this property to derive a learning algorithm, whereby when the agent takes an action we update its value function to reduce its temporal difference error. Thus, for a given (s, a,r,s 0 , a 0 ) tuple from the environment, we can perform the following update

Qπ(s, a) = Qπ(s, a) + α[(r + γQπ(s 0 , a 0 )) − Qπ(s, a)],

This is usually accomplished through the use of a value function, which maps states and actions to return. For example

Qπ(s, a) → Rπ.

he agent attempts to learn a policy (mapping from states to actions) π. A policy tells the agent what to do where

Rather than attempting to minimise its immediate reward, the agent wishes to find a policy that maximises its cumulative reward (termed return): Rπ = X∞ i=0 γ i ri+

if mappings describe agents, then ideal mappings describe ideal agents

Specifying which action an agent ought to take in response to any given percept sequence provides a design for an ideal agentتحديد الإجراء الذي يجب على الوكيل اتخاذه استجابة لأي تسلسل إدراكي معين يوفر تصميمًا لعامل مثالي

• Two kinds of failure

Standard failure: indicates no solution Cut off: indicates no solution within the depth limit

2 Weak AI: can machines act intelligently?

The Weak AI claim is the claim that a computer program can be built that acts intelligently; in other words, that is able to solve problems or handle tasks that would typically be considered to require intelligence

Define Architecture

The action program will run on some sort of computing device which is called as Architecture

Goal-based agents:

The agent has a purpose and the action to be taken depends on the current state and on what it tries to accomplish (the goal).

environment dispaly

The environment also displays the camera image on a large display screen that the human can watch, and superimposes on the image a computer graphics rendering of the software agent.تعرض البيئة أيضًا صورة الكاميرا على شاشة عرض كبيرة يمكن للإنسان مشاهدتها ، ويقوم بتكبيرها على صورة عرض رسومات الكمبيوتر لعامل البرنامج.

problems in the space

The initial state that the agent knows itself to be i he term operator (An alternate formulation uses a successor function S

WHAT is AI?

The study of the computations that make it possible to perceive, reason, and act"دراسة الحسابات التي تجعل من الممكن إدراك ، العقل ، والتصرف "

What is a disjoint pattern database?

The sum of the two costs is still a lower bound on the cost of solving the entire problem is called a disjoint pattern database.

PROPOSITIONAL LOGIC: A VERY SIMPLE LOGIC

The syntax of prepositional logic is simple

. Define the term utility

The term utility is used in the sense of "the quality of being useful .", utility of a state is relative to the agents, whose preferences the utility function is supposed to represent

Define Total Turing Test?

The test which includes a video signals so that the interrogator can test the perceptual abilities of the machine.

Depth-first search has very modest memory requirements.

The time complexity for depth-first search is O(bm ).

Define search tree

The tree which is constructed for the search process over the state space is called search tree.

Mention the criteria for the evaluation of search strategy.

There are 4 criteria: Completeness, time complexity, space complexity, optimality

Simple Reflex Agent

These agents select actions on the basis of the current percept, ignoring the rest of the percept historyهذه العوامل تحدد الإجراءات على أساس النظرة الحالية ، متجاهلة ما تبقى من تاريخ الإدراك

Define backward chaining.

This algorithm works backward from the goal, chaining through rules to find known facts that support the proof.

.Define joint probability distributio

This completely specifies an agent's probability assignments to all propositions in the domain.The joint probability distribution p(x1,x2,--------xn) assigns probabilities to all possible atomic events;where X1,X2------Xn 10 =variables

What is a pattern database?

This database is the storage of exact solution costs for every possible sub problem instance.

PREFERENCES

To make such choices, an agent must first have preferences between the different possible outcomes of the various plans.

component of agent

agent = architecture + program

two evaluation functions simply by summing them

f(n) = g(n) + h(n).

Define Heuristic function, h (n)

h (n) is defined as the estimated cost of the cheapest path from node n to a goal node.

a heuristic function

h(n) = estimated cost of the cheapest path from the state at node n to a goal state.

When considering a particular input, execution starts at the root node

or each test, we follow the edge corresponding to that attribute's value for the given input. For example, given input data [PassedBoundary = Yes, Bounced = No] we first test PassedBoundary; following its Yes edge, we test Bounced; following its No edge, we can conclude Boundary, 6 runs.

.Define Prior Probability

p(a) for the Unconditional or Prior Probability Is That the Proposition A is True. It is important to remember that p(a) can only be used when there is no other informatio

SYNTAX AND SEMANTICS

propositional logic every expression is a sentence

y. Another common form is a metaphor,

variants of which are common in spoken language. For example, this mobile phone model is very hot right now implies the phone is selling well, but says nothing about its temperature

.what is the need for utility theory in uncertainty

Utility theory says that every state has a degree of usefulness, or utility to in agent, and that the agent will prefer states with higher utility. The use utility theory to represent and reason with preferences

Operators:A set of descriptions of actions, using the action representation described in Chapter 7. For example, here is a successor-state axiom involving the Buy(Milk) action:

V a, s Have(Milk, Result(a,.?)) O [(a = Buy(Milk) A At(Supermarket, s) V (Have(Milk, s)Aa ^Dmp(Milk))]

.Give the Baye's rule equatio

W.K.T P(A ^ B) = P(A/B) P(B) -------------------------- 1 P(A ^ B) = P(B/A) P(A) --------------------------

What is the advantage of memory bounded search techniques?

We can reduce space requirements of A* with memory bounded algorithm such as IDA* & SMA*.

Complex sentences

We can use logical connectives to construct more complex sentences,

Thinking humanly: The cognitive modelling approach

We need to get inside the actual workings of human minds. There are two ways to do this: through introspection—trying to catch our own thoughts as they go by—or through psychological experiments.نحن بحاجة إلى الدخول في العمل الفعلي للعقول البشرية. هناك طريقتان للقيام بذلك: من خلال الاستبطان - محاولة التقاط أفكارنا الخاصة أثناء مرورهم - أو من خلال التجارب النفسية.

Philosophical claims about AI can be broken into two distinct areas,

Weak AI claims and Strong AI claims

The when of evaluating performance is also important.

f we measured how much dirt the agent had cleaned up in the first hour of the day, we would be rewarding those agents that start fast (even if they do little or no work later on), and punishing those that work consistentlyلقد قمنا بقياس مقدار القاذورات التي قام العامل بتطهيرها في الساعة الأولى من اليوم ، وسوف نكافئ هؤلاء الوكلاء الذين يبدأون سريعاً (حتى لو لم يفعلوا شيئاً يذكر أو لا عمل في وقت لاحق) ، ومعاقبتهم على العمل باستمرار

Properties of Bidirectional Search:

dvantages: Reduce time complexity and space complexity Disadvantages: The space requirement is the most significant weakness of bi-directional search. If two searches do not meet at all, complexity arises in the search technique. In backward search calculating predecessor is difficult task. If more than one goal state exists then explicitly, multiple state searches are required.

performance measure use

establish a standard of what it means to be successful in an environment and use it to measure the performance of agentsوضع معيار لما يعنيه النجاح في بيئة واستخدامه لقياس أداء الوكلاء

An atomic sentence predicate(term1 ,...,termn ) is true

iff the objects referred to by term1 ,...,termn are in the relation referred to by predicate

Value functions

ince we wish to optimise a measure of return (rather than reward), it makes sense for us to attempt to model return

the basic ideas of planning

including the need to divide complex problems into subgoals whose solutions can be combined to provide a solution for the complete problem

A path cost function

is a function that assigns a cost to a path. In all cases we will consider, the cost of a path is the sum of the costs of the individual actions along the path

This type of value function

is known as a state-action value function because it stores the return for a state-action pair, assuming the agent follows policy

A complete plan

is one in which every precondition of every step is achieved by some other step.

The probability of a proposition

is only defined if the agent conditions on the values of all decision variables.

A problem

is really a collection of information that the agent will use to decide what to do

A path in the state space

is simply any sequence of actions leading from one state to another.

The most famous artificial environment

is the Turing Test environment, in which the whole point is that real and artificial agents are on equal footing, but the environment is challenging enough that it is very difficult for a software agent to do as well as a human.هي بيئة اختبار تورينج ، حيث النقطة الأساسية هي أن العوامل الحقيقية والاصطناعية على قدم المساواة ، ولكن البيئة صعبة بما فيه الكفاية بحيث يصعب على وكيل البرامج أن يفعله كما يفعل الإنسان.

The logical level

is the level at which the knowledge is encoded into sentences. For example, the taxi might be described as having the logical sentence Links(GGBridge, SF, Marin) in its knowledge base

The knowledge level or epistemological level

is the most abstract; we can describe the agent by saying what it knows. For example, an automated taxi might be said to know that the Golden Gate Bridge links San Francisco and Marin County. If TELL and ASK work correctly, then most of the time we can work at the knowledge level and not worry about lower levels

reflex agent with internal state

is the same that agent keep track of the worldهو نفس ذلك الوكيل يتتبع العالم

Like any other emerging and potentially transformative technology, AI is having and will continue to have, a significant impact on society. Russell and Norvig list six potential threats to society

job losses too much (or too little) leisure time humans might lose their sense of 'uniqueness' potential loss of privacy potential loss of accountability AI systems might take over the world.

the relation of entailment between a knowledge base KB and a sentence a is pronounced "KB entails a" and written as

kb=|k

What is RBFS?

keeps track of the f-value of the best alternative path available from any ancestor of the current node. RBFS remembers the f-value of the best leaf in the forgotten sub tree and therefore decide whether its worth re expanding the sub tree sometimes later

training set and testing set

lassifiers are usually tested on a different set of labelled examples to the ones they are trained on. Thus, the training set of examples is used for learning, and the test set of data is used to measure how well the learning system is able to generalise to unseen example

propositional logic

mbols represent whole propositions (facts); for example, D might have the interpretation "the wumpus is dead." which may or may not be a true proposition

Propositional logic is compositional

meaning of B1,1 ∧ P1,2 is derived from meaning of B1,1 and of P1,2

Semantic ambiguity

most often results from syntactic ambiguity, but can also occur even within a single parse tree

Acting humanly: The Turing Test approach

natural language processing to enable it to communicate successfully in English (or some other human language); <C> knowledge representation to store information provided before or during the interrogation; <) automated reasoning to use the stored information to answer questions and to draw new conclusions; <) machine learning to adapt to new circumstances and to detect and extrapolate patterns. معالجة اللغة الطبيعية لتمكينها من التواصل بنجاح باللغة الإنجليزية (أو بعض اللغات البشرية الأخرى) ؛ <C> تمثيل المعرفة لتخزين المعلومات المقدمة قبل أو أثناء الاستجواب ؛ <) الاستدلال الآلي لاستخدام المعلومات المخزنة للإجابة على الأسئلة وللاستخلاص استنتاجات جديدة ؛ <) التعلم الآلي للتكيف مع الظروف الجديدة وكشف الأنماط واستقراءها.

total Turing Test

ncludes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects "through the hatchيتضمن إشارة فيديو لكي يتمكن المحقق من اختبار القدرات الإدراكية للموضوع ، وكذلك فرصة للمحقق لتمرير الأشياء المادية "من خلال الفتحة

A* is optimally efficient

no other optimal algorithm is guaranteed to expand fewer nodes than A*.

define the state space of the problem

the set of all states reachable from the initial state by any sequence of actions

Utility-based agents

try to maximize their own expected "happiness"حاول زيادة "السعادة" المتوقعة

There are several synonyms for valid sentences.

use the terms analytic^ sentences or tautologies for valid sentences

a simple planning agent

very similar to a problem-solving agent

To describe information gain

we require a few new concepts. The information I contained in a set of examples can be expressed as a measure of the fraction of positive and negative examples.

Lexical ambiguity

where a single word can have more than one meaning

Perception

where a speech pattern (or optical pattern, in the case of optical text processing) is obtained by a sensor, and must be converted to a symbolic representation

Reinforcement learning,

where the system must interact with an environment, and is given a reward for each action it takes

Syntactic ambiguity

where there is more than one valid parse tree for a single sentence

using connectives and quantifiers,

which allow sentences to be written about everything in the universe at once

Goal Formulation

the process of developing specific goals for the planning period

Building systems that act like humans

"The art of creating machines that perform functions that require intelligence when performed by people" -- Kurzweil, 1990 "The study of how to make computers do things at which, at the moment, people are better" -- Rich and Knight, 1991

Building systems that think like humans

"The exciting new effort to make computers think ... machines with minds, in the full and literal sense" -- Haugeland, 1985 "The automation of activities that we associate with human thinking, ... such as decision-making, problem solving, learning, ..." -- Bellman, 1978

Building systems that think rationally

"The study of mental faculties through the use of computational models" -- Charniak and McDermott, 1985 "The study of the computations that make it possible to perceive, reason, and act" -- Winston, 1992

Thinking Rationally- The laws of thought approach

Capture ``correct'' reasoning processes" A loose definition of rational thinking: Irrefutable reasoning process How do we do this Develop a formal model of reasoning (formal logic) that "always" leads to the "right" answer Implement this model

Partially Observable:

Ex: Automated taxi cannot see what other devices are thinking

Stochastic:

Ex: taxi driving is clearly stochastic in this sense, because one can never predict the behavior of the traffic exactly.

The sequence of steps done by intelligent agent to maximize the performance measure:

Goal Formulation: based on the current situation and the agent's performance measure, it is the first step in problem solving. Problem Formulation: it is the process of deciding what actions and states to consider, given a goal. Search: the process of looking for different sequence. Solution: A search algorithm takes a problem as input and returns a solution in the form of an action sequence.

Semi dynamic:

If the environment does not change for some time, then it changes due to agent's performance is called semi dynamic environment.

What are the three levels in describing knowledge based agent?

Logical level Implementation level Knowledge level or epistemological level

List the various type of agent program.

Simple reflex agent program. Agent that keep track of the world. Goal based agent program. Utility based agent program

Iterative deepening depth-first search It is a strategy that steps the issue of choosing the best path depth limit by trying all possible depth limit

Uses depth-first search Finds the best depth limit Gradually increases the depth limit; 0, 1, 2, ... until a goal is found

Building systems that act rationally

"A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes" -- Schalkoff, 1990 "The branch of computer science that is concerned with the automation of intelligent behavior" -- Luger and Stubblefield, 1993

Syntactic Analysis

"I ate spaghetti with chopsticks" vs. "I ate spaghetti with meatballs

Acting humanly

"If it looks, walks, and quacks like a duck, then it is a duck"

• Speech Recognition

"recognize speech" vs. "wreck a nice beach" - "youth in Asia" vs. "euthanasia"

agent-based software engineering

(Genesereth and Ketchpel, 1994) attempts to make all sorts of systems and resources interoperable by providing a declarative interface based on first-order logic

Inside the room sits a man who only speaks English, with a large stack of books with English rules for translating Chinese symbols

(which are just 'squiggles' to the man) to other Chinese symbols. When a new piece of paper enters the room, he follows the rules in the books to produce a new page, and pushes that page through the output slot.

Goal formulation, based on the current situation

, is the first step in problem solving. As well as formulating a goal, the agent may wish to decide on some other factors that affect the desirability of different ways of achieving the goal

Internal nodes are always tests, consisting of the name of one of the attributes

, along with an outgoing edge for each of its possible attribute values. Figure 7.1 shows a simple decision tree for determining whether a run in cricket was a boundary, and if so whether it scored 4 or 6. Decision nodes are shown in bold with rounded edges

Decisions are always leaf nodes labelled by one of the classes the decision tree can output

, and reaching one for a particular input indicates that the tree returns that class for that input.

Semantic Analysis

- "The dog is in the pen." vs. "The ink is in the pen." - "I put the plant in the window" vs. "Ford put the plant in Mexico"

Semantics concerns the (literal) meaning of words, phrases, and sentences

- "plant" as a photosynthetic organism - "plant" as a manufacturing facility - "plant" as the act of sowing

Single Agent (vs. Multi Agent)

- Competitive, cooperative multi-agent environments - Communication is a key issue in multi agent environments.

Static (vs. Dynamic

- Environment doesn't change as the agent is deliberating - Semi dynamic

Pragmatic Analysis

- From "The Pink Panther Strikes Again": - Clouseau: Does your dog bite? Hotel Clerk: No. Clouseau: [bowing down to pet the dog] Nice doggie. [Dog barks and bites Clouseau in the hand] Clouseau: I thought you said your dog did not bite! Hotel Clerk: That is not my dog

Four components of problem definition

- Initial state - that the agent starts in - Possible Actions • Uses a Successor Function - Returns <action, successor> pair • State Space - the state space forms a graph in which the nodes are states and arcs between nodes are actions. • Path - Goal Test - which determine whether a given state is goal state - Path cost - function that assigns a numeric cost to each path. • Step cost

Deterministic (vs. Stochastic)

- Next state of the environment is completely determined by the current state and the action executed by the agent - Strategic environment (if the environment is deterministic except for the actions of other agent.)

first-order logic (like natural language) assumes the world contains

- Objects: people, houses, numbers, colors, baseball games, wars

• Syntax concerns the proper ordering of words and its affect on meaning

- The dog bit the boy. - The boy bit the dog. - * Bit boy dog the the. - Colorless green ideas sleep furiously.

Time and space complexity are measured in

- b - maximum branching factor of the search tree - m - maximum depth of the state space - d - depth of the least cost solution

Characteristics OF Simple reflex agen

- no plan, no goal - do not know what they want to achieve - do not know what they are doing Condition-action rule - If condition then action Ex: medical diagnosis system.

2. State the reason why first order, logic fails to cope with that the mind like medical diagno

. Three reasons a.laziness: o it is hard to lift complete set of antecedents of consequence, needed to ensure and exceptionless rule. b. Theoritical Ignorance: o medical science has no complete theory for the do

g(n) gives the path cost from the start node to node n, and h(ri) is the estimated cost of the cheapest path from n to the goal

/(«) = estimated cost of the cheapest solution through n

Logics

1. A formal system for describing states of affairs, consisting of (a) the syntax of the language, which describes how to make sentences, and (b) the semantics of the language, which states the systematic constraints on how sentences relate to states of affairs.

Four types of agents:

1. Simple reflex agent 2. Model based reflex agent 3. goal-based agent 4. utility-based agent

What are the factors that a rational agent should depend on at any given time?

1. The performance measure that defines degree of success. 2. Ever thing that the agent has perceived so far. We will call this complete perceptual history the percept sequence. 3. When the agent knows about the environment. 4. The action that the agent can perform

Properties of Bidirectional Search:

1. Time Complexity: O(b d/2) 2. Space Complexity: O(b d/2) 3. Complete: Yes 4. Optimal: Yes

Uniform cost search

Breadth-first search finds the shallowest goal state, but this may not always be the least-cost solution for a general path cost function.

Goal state: A logical query asking for suitable situations. For the shopping problem, the query would be

3 s At(Home, s) A Have(Milk, s) A Have(Bananas, s) A Have(Drill, s)

lexical ambiguity

A common example of this is the word run, which is most commonly a verb (meaning the physical act of running (I like to run), or the running of a business (I run a company), or the running of a program (please run the spell checker)) but can also be a noun (he went for a run, or show me the output from your program's third run).

Depth Limited Search

A depth first search with a predetermined depth limit. Incomplete if limit is less than depth. Not optimal if limit is greater than depth.

WHAT is AI?

A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes"مجال الدراسة الذي يسعى إلى شرح ومحاكاة السلوك الذكي من حيث العمليات الحسابية

2-Fully Observable vs. Partially Observable

A fully observable AI environment has access to all required information to complete target task. Image recognition operates in fully observable domains. Partially observable environments such as the ones encountered in self-driving vehicle scenarios deal with partial information in order to solve AI problems.بيئة منظمة العفو الدولية التي يمكن ملاحظتها بالكامل يمكنها الوصول إلى جميع المعلومات المطلوبة لإكمال المهمة المستهدفة. يعمل التعرف على الصور في نطاقات قابلة للملاحظة بشكل كامل. البيئات التي يمكن ملاحظتها جزئيًا مثل تلك التي واجهتها في سيناريوهات المركبة ذاتية القيادة تتعامل مع معلومات جزئية لحل مشكلات الذكاء الاصطناعي

Define Path Cost

A function that assigns a numeric cost to each path, which is the sum of the cost of the each action along the path.

. Define Local maxima

A local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum.

Define Evaluation function, f(n)

A node with the lowest evaluation is selected for expansion, because evaluation measures distance to the goal.

Define Path

A path in the state space is a sequence of state connected by sequence of actions.

What is Released problems

A problem with fewer restrictions on the actions is called a relaxed problem

Define probability distribution

Eg. P(weather) = (0.7,0.2,0.08,0.02). This type of notations simplifies many equations

RATIONAL AGENT:

A rational agent is one that can take the right decision in every situation. Performance measure: a set of criteria/test bed for the success of the agent's behavior. The performance measures should be based on the desired effect of the agent on the environmen

rational agent

A rational agent is one that does the right thingالعامل العقلاني هو الذي يفعل الشيء الصحيح

What is autonomy?

A rational agent should be autonomous. It should learn what it can do to compensate for partial (or) in correct prior knowledge.

Define true sentence

A sentence is true under a particular interpretation if the state of affairs it represents is the case.

Terms

A term is a logical expression that refers to an object. Constant symbols are therefore terms. Sometimes, it is more convenient to use an expression to refer to an object

Predicate symbols: Round, Brother

A tuple is a collection of objects arranged in a fixed order

Proof of the completeness of A*

A* is complete on locally finite graphs

Define A* search

A* search evaluates nodes by combining g(n), the cost to reach the node and h(n), the cost to get from the node to the goal. f(n) = g(n) + h(n)

Complexity of A*

A* search is complete, optimal, and optimally efficient among all such algorithms is rather satisfying

Thinking humanly

AI means acting humanly, i.e., acting like a person. The classic example of this is the "Turing test" (details on a later slide). - AI means thinking humanly, i.e., thinking like a person. The field of Cognitive Science delves into this topic, trying to model how humans think.الذكاء الاصطناعى (بالإنجليزية: AI): تعني التصرف إنسانياً ، بمعنى ، التصرف كشخص. المثال الكلاسيكي لذلك هو "اختبار تورينج" (تفاصيل حول شريحة لاحقة). - الذكاء الاصطناعي يعني التفكير الإنساني ، أي التفكير كشخص. يتعمق مجال العلوم المعرفية في هذا الموضوع ، ويحاول أن يصمم كيف يفكر البشر

Define AND -Elimination rule in propositional logic

AND elimination rule states that from a given conjunction it is possible to inference any of the conjuncts.

What are the two we use to query and answer in knowledge base?

ASK and TELL

Acting Rationally

Act so that desired goals are achieved The rational agent approach (this is what we'll focus on in this course) Figure out how to make correct decisions, which sometimes means thinking rationally and other times means having rational reflexes correct inference versus rationality reasoning versus acting; limited rationality

Acting rationally: The rational agent approach

Acting rationally means acting so as to achieve one's goals, given one's beliefs. An agent is just something that perceives and actsالتصرف العقلاني يعني التصرف لتحقيق أهداف المرء ، مع الأخذ بعين الاعتبار معتقداته. الوكيل هو مجرد شيء يدرك ويتصرف

Characteristics of goal based agent

Action depends on the goal. (consideration of future) - e.g. path finding - Fundamentally different from the condition-action rule. - Search and Planning - Solving "car-braking" problem? - Yes, possible ... but not likely natural.

Learning Agent

Agent Example A file manager agent. Sensors: commands like ls, du, pwd. Actuators: commands like tar, gzip, cd, rm, cp, etc. Purpose: compress and archive files that have not been used in a while. Environment: fully observable (but partially observed), deterministic (strategic), episodic, dynamic, discrete.

example of an agent and it operation and goals

Agent Type taxi driver Precepts cameras speadometer and microphone Actions Steer, accelerate,brake, talk to passenger Goals (Safe, fast, legal,comfortable trip, maximize profits) Environment Roads, other ,traffic, pedestrians, customersنوع الوكيل سائق سيارة الأجرة Precepts الكاميرات speadometer والميكروفون إجراءات Steer، accelerate، brake، talk to passenger Goals (آمنة ، سريعة ، قانونية ، رحلة مريحة ، تعظيم الأرباح) البيئة طرق ، أخرى ، حركة مرور ، مشاة ، عملاء

Define Agent Program.

Agent function for an agent will be implemented by agent program.

Give the structure of agent in an environment?

Agent interacts with environment through sensors and actuators. An Agent is anything that can be viewed as perceiving (i.e.) understanding its environment through sensors and acting upon that environment through actuators.

How agent should act?

Agent should act as a rational agent. Rational agent is one that does the right thing, (i.e.) right actions will cause the agent to be most successful in the environment.

Why does uncertainty arise ?

Agents almost never have access to the whole truth about their environment. Agents cannot find a caterorial answer. Uncertainty can also arise because of incompleteness, incorrectness in agents understanding of properties of environmen

Learning Agents

Agents capable of acquiring new competence through observations and actions

Simple reflex agent

Algorithm Explanation: Interpret - Input: Function generates an abstracted description of the current state from the percept. RULE- MATCH: Function returns the first rule in the set of rules that matches the given state description. RULE - ACTION: The selected rule is executed as action of the given percep

cognitive skills"

All the "cognitive skills" needed for the Turing Test are there to allow rational actions. Thus, we need the ability to represent knowledge and reason with it because this enables us to reach good decisions in a wide variety of situations.هناك جميع "المهارات المعرفية" اللازمة لاختبار تورينج هناك للسماح بإجراءات عقلانية. وبالتالي ، نحن بحاجة إلى القدرة على تمثيل المعرفة والعقل بها لأن هذا يمكننا من الوصول إلى قرارات جيدة في مجموعة متنوعة من الحالات.

Computational Linguistics

Also concerns how computational methods can aid the understanding of human language

BI-DIRECTIONAL SEARCH

Alternate searching from the start state toward the goal and from the goal state toward the start. • Stop when the frontiers intersect. • Works well only when there are unique start and goal states. • Requires the ability to generate "predecessor" states. • Can (sometimes) lead to finding a solution more quickly.

Define Agent.

An Agent is anything that can be viewed as perceiving (i.e.) understanding its environment through sensors and acting upon that environment through actuators.

Define Omniscience.

An Omniscience agent knows the actual outcome of its actions and can act accordingly

agent in ai

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through العامل هو أي شيء يمكن رؤيته كإدراك بيئته من خلال أجهزة الاستشعار والعمل على تلك البيئة من خلال المستجيبين

Agent Autonomy:

An agent is omniscient if it knows the actual outcome of its actions. Not possible in practice. An environment can sometimes be completely known in advance.

Define Percept Sequence.

An agent's choice of action at any given instant can depend on the entire percept sequence observed to elate.

.What is an atomic event

An atomic event is an assignment of particular values to all variables, in other words, the complete specifications of the state of domain

Define a Complete inference procedure

An inference procedure is complete if it can derive all true conditions from a set of premises

Define an inference procedure

An inference procedure reports whether or not a sentence is entiled by knowledge base provided a knowledge base and a sentence .An inference procedure 'i' can be described by the sentences that it can derive. If i can derive from knowledge base, we can write. KB --Alpha is derived from KB or i derives alpha from KB

An omniscient agent

An omniscient agent knows the actual outcome of its actions, and can act accordingly; but omniscience is ; impossible in realityيعرف وكيل كلي العلم النتيجة الفعلية لأعماله ، ويمكن أن يعمل وفقا لذلك ؛ لكن كلي العلم هو مستحيل في الواقع

Define an Omniscient agent.

An omniscient agent knows the actual outcome of its action and can act accordingly; but omniscience is impossible in reality.

Simple reflex agent

Definition: SRA works only if the correct decision can be made on the basis of only the current percept that is only if the environment is fully observable.

If one accepts materialism, then the question comes down to whether or not the functional or physical properties of the brain cause minds.

Biological naturalism holds that minds are a result of the physical properties of the brain—neurons cause minds—in which case a simulation would just be a simulation. By contrast, functionalism holds that mental states are caused by the computational properties of the brain—it is their causal role in input and output that matters.

AI definition

Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.

The argument from disability can be paraphrased as: but a machine can never do X, for some X, such as 'write a poem' or 'play a game of chess'

As the preceding example illustrates, in many cases some time after a particular activity is claimed to be impossible a system that can do it is demonstrated. Although there are many important activities that computers have not yet been shown to be able to do, it is important not to confuse not yet knowing how to create such a system with such a system being impossible.

BASIC REPRESENTATIONS FOR PLANN

At(Home) A -^Have(Milk) A ^Have(Bananas) A ^Have(Drill) A • • •

Goals are also described by conjunctions of literals

At(Home) A Have(Milk) A Have(Bananas} A Have(Drill)

Initial state: An arbitrary logical sentence about a situation 50. For the shopping problem, this might be

At(Home, 50) A ~^Have(Milk, 50) A -^Have(Bananas, S0) A -iHave(Drill, S0)

Atomic sentences

Atomic sentence = predicate (term1 ,...,termn ) or term1 = term2

Define Atomic sentence

Atomic sentence is formed by both objects and relations

Differentiate BFS & DFS.

BFS means breath wise search. Space complexity is more. Do not give optimal solution Queuing fn is same as that of queue operator DFS means depth wise search. Space complexity is less Gives optimal solution Queuing fn is somewhat different from queue operator.

What is Best First Search?

Best First Search is an instance of the general TREE SEARCH or GRAPH SEARCH algorithm in which a node is selected for expansion based on an evaluation function, f(n).

List the various informed search strategy.

Best first search -greedy search ,A* search Memory bounded search-Iterative deepening A*search -simplified memory bounded A*search -Iterative improvement search -hill climbing -simulated annealing

examples of sentences used

Brother(Richard,John) A Brother(John, Richard) is true just when John is the brother of Richard and Richard is the brother of John. • Older(John, 30) V Younger(John, 30) is true just when John is older than 30 or John is younger than 30. • Older(John, 30) => -^Younger(John, 30) states that if John is older than 30, then he is not younger than 30.3 • -^Bmther(Robin, John) is true just when Robin is not the brother of John.

Supervised learning

Classification, where the outputs that the system is trying to learn are a set of discrete classes.

Typically, ∧ is the main connective with ∃

Common mistake: using ⇒ as the main connective with ∃: ∃x Crown(x) ⇒ OnHead(x,John) is true even if there is anything which is not a crown

Typically, ⇒ is the main connective with ∀

Common mistake: using ∧ as the main connective with ∀: ∀x King(x) ∧ Person(x) means "Everyone is a king and everyone is a person

3-Competitive vs. Collaborative

Competitive AI environments face AI agents against each other in order to optimize a specific outcome. Games such as GO or Chess are examples of competitive AI environments. Collaborative AI environments rely on the cooperation between multiple AI agents. Self-driving vehicles or cooperating to avoid collisions or smart home sensors interactions are examples of collaborative AI environments.تواجه بيئات الذكاء الاصطناعي التنافسية عوامل الذكاء الاصطناعي ضد بعضها البعض من أجل تحسين نتيجة محددة. ألعاب مثل GO أو الشطرنج هي أمثلة لبيئات منظمة العفو الدولية التنافسية. تعتمد بيئات الذكاء الاصطناعى التعاونى على التعاون بين العديد من وكلاء الذكاء الاصطناعى. المركبات ذاتية القيادة أو المتعاونة لتجنب الاصطدامات أو تفاعلات أجهزة الاستشعار المنزلية الذكية هي أمثلة على بيئات منظمة العفو الدولية التعاونية.

Properties of Breadth-First Search

Complete - Yes if b (max branching factor) is finite • Time - 1 + b + b2 + ... + bd + b(bd -1) = O(bd+1) - exponential in d • Space - O(bd+1) - Keeps every node in memory - This is the big problem; an agent that generates nodes at 10 MB/sec will produce 860 MB in 24 hours • Optimal - Yes (if cost is 1 per step); not optimal in general

Properties of Depth-Limited Search

Complete - Yes if l < d • Time - N(IDS)=(d)b+(d-1)b²+..........................+(1) - O(bl ) • Space - O(bl) • Optimal - No if l > d

1-Complete vs. Incomplete

Complete AI environments are those on which, at any give time, we have enough information to complete a branch of the problem. Chess is a classic example of a complete AI environment. Poker, on the other hand, is an incomplete environments as AI strategies can't anticipate many moves in advance and, instead, they focus on finding a good 'equilibrium" at any given time

evaluate strategies in terms of four criteria:

Completeness: is the strategy guaranteed to find a solution when there is one? <) Time complexity: how long does it take to find a solution? <) Space complexity: how much memory does it need to perform the search? <} Optimality: does the strategy find the highest-quality solution when there are several different solutions?7

Complex sentences

Complex sentences are made from atomic sentences using connectives ¬S, S1∧ S2 , S1∨ S2 , S1⇒ S2 , S1⇔S2 ,

What are the capabilities computers needs to pass total Turing test?

Computer Vision Robotics

more detailed explanation of each element, describing both syntax and semantics

Constant symbols: A, B, C, John

Syntax of FOL: Basic elements

Constants : KingJohn, 2, ... • Predicates: Brother, >,... • Functions : Sqrt, LeftLegOf,... • Variables x, y, a, b,... • Connectives ¬, ⇒, ∧, ∨, ⇔ • Equality = • Quantifiers ∀, ∃

Four important classes of generative grammars are, in descending order of expressiveness

Context-sensitive grammars, which restrict the right-hand side (replacement pattern) to have at least as many symbols as the left-hand side (search pattern). This allows us to specify the context of a symbol when writing a rewrite rule for it. The example rewrite rule given above is a context-sensitive rewrite rule, because it restricts the replacement of the non-terminal A by the terminal x to those cases where A is followed by B.

BI-DIRECTIONAL SEARCH

Definition: It is a strategy that simultaneously searches both the directions (i.e) forward from the initial state and backward from the goal state and stops when the two searches meet in the middle.

BREADTH-FIRST SEARCH

Definition: The root node is expanded first, and then all the nodes generated by the node are expanded. • Expand the shallowest unexpanded node • Place all new successors at the end of a FIFO queue

DEPTH-LIMITED SEARCH

Definition: A cut off (Maximum level of the depth) is introduced in this search technique to overcome the disadvantage of Depth First Search. The cut off value depends on the number of states. DLS can be implemented as a simple modification to the general tree search algorithm or the recursive DFS algorithm.

Discrete (vs. Continuous)

Depends the way time is handled in describing state, percept, actions • Chess game : discrete • Taxi driving : continuous

Lexicon

Det → the | a | that | this Noun → book | flight | meal | money Verb → book | include | prefer Pronoun → I | he | she | me Proper-Noun → Houston | NWA Aux → does Prep → from | to | on | near | through Grammar L

6-Deterministic vs. Stochastic

Deterministic AI environments are those on which the outcome can be determined base on a specific state. In other words, deterministic environments ignore uncertainty. Most real world AI environments are not deterministic. Instead, they can be classified as stochastic. Self-driving vehicles are a classic example of stochastic AI processes.بيئات الذكاء الاصطناعي الحتمية هي تلك التي يمكن تحديد النتيجة فيها على أساس حالة معينة. وبعبارة أخرى ، تتجاهل البيئات الحتمية عدم اليقين. معظم بيئات منظمة العفو الدولية في العالم الحقيقي ليست حتمية. بدلا من ذلك ، يمكن تصنيفها على أنها عشوائية. المركبات ذاتية القيادة هي مثال كلاسيكي على العمليات العشوائية لمنظمة العفو الدولية.

Thinking humanly: cognitive modeling

Develop a precise theory of mind, through experimentation and introspection, then write a computer program that implements it Example: GPS - General Problem Solver (Newell and Simon, 1961) trying to model the human process of problem solving in general

static environment

Devices in which additional hardware cannot easily be added or attached.لأجهزة التي لا يمكن إضافة أو إرفاق أجهزة إضافية بسهولة.

5-Discrete vs. Continuous

Discrete AI environments are those on which a finite [although arbitrarily large] set of possibilities can drive the final outcome of the task. Chess is also classified as a discrete AI problem. Continuous AI environments rely on unknown and rapidly changing data sources. Vision systems in drones or self-driving cars operate on continuous AI environments.إن بيئات الذكاء الاصطناعي المنفصلة هي تلك التي يمكن لمجموعة من الاحتمالات المحدودة [رغم كونها عشوائية] أن تقود النتيجة النهائية للمهمة. يتم تصنيف الشطرنج أيضًا على أنه مشكلة AI منفصلة. تعتمد بيئات الذكاء الاصطناعي المستمر على مصادر بيانات غير معروفة ومتغيرة بسرعة. أنظمة الرؤية في الطائرات بدون طيار أو السيارات ذاتية القيادة تعمل في بيئات الذكاء الاصطناعي المستمر.

What is Environment Class (EC) and Environment Generator (EG)?

EC - It is defined as a group of environment. EG - It selects the environment from environment class in which the agent has to Run

Define a Sentence?

Each individual representation of facts is called a sentence. The sentences are expressed in a language called as knowledge representation language.

What is metalevel state space?

Each state in a metalevel state space captures the internal state of a program that is searching in an object level state space.

Best-first search

Expands nodes base on evaluation function. Use priority queue with estimated cost.

Define syntactic sugar.

Extension to the standard syntax (i.e.) procedure that does not change the semantics (i.e.) meaning is called syntactic sugar.

Define FOL

FOL is a first order logic. It is a representational language of knowledge which is powerful than propositional logic (i.e.) Boolean Logic. It is an expressive, declarative, compositional language

First-order logic commits to the representation of worlds

First-order logic commits to the representation of worlds

Define Ideal Rational Agent.

For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built in knowledge the agent has.

ideal rational agent:

For each possible percept sequence, an ideal rational agent should do whatever action is expected to maximize its performance measure, on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent hasلكل تسلسل إدراكي محتمل ، ينبغي أن يقوم العامل العقلاني المثالي بأي إجراء من المتوقع أن يزيد من قياس أدائه ، على أساس الأدلة المقدمة من تسلسل الإدراك الحسي وأي معرفة مضمنة يملكها العامل.

Syntactic ambiguity example

For example, the phrase I cut the apple with the knife could be parsed as (I cut the apple) (with the knife) or (I cut) (the apple with the knife). Both are valid parse trees, but since we know that apples do not carry knives, and (that cutting is something that is done with a knife,) the second is less likely than the first.1

Properties of Task Environment:

Fully Observable (vs. Partly Observable) - Agent sensors give complete state of the environment at each point in time - Sensors detect all the aspect that is relevant to the choice of action. - An environment might be partially observable because of noisy and inaccurate sensors or apart of the state are simply missing from the sensor data.

Goal Formulation

Goal formulation, based on the current situation, is the first step in problem solving. As well as formulating a goal, the agent may wish to decide on some other factors that affect the desirability of different ways of achieving the goa

what are the goal benefit

Goals such as this help organize behavior by limiting the objectives that the agent is trying to achieveتساعد أهداف كهذه في تنظيم السلوك من خلال الحد من الأهداف التي يحاول الوكيل تحقيقها

For each assignment of values to all decision variables, a probability distribution over the set of worlds satisfying that assignment is given

I The probability distribution is defined over all possible assignments to the random variables. I The probabilities for all possible worlds satisfying one assignment to the decisions variables sum to 1.

n any case, most AI researchers do not believe that arguments about Strong AI are important:

I constructing intelligent systems is, in their view, sufficiently interesting, whether or not such systems match our current meaning of the word 'mind'.

Natural language is highly ambiguous and must be disambiguated

I saw the man on the hill with a telescope. - I saw the Grand Canyon flying to LA. - Time flies like an arrow. - Horse flies like a sugar cube. - Time runners like a coach. - Time cars like a Porsche.

Let fA+ be the fraction of the examples in example set A that are positive, and fA− the proportion that are negative. Then the information contained in A can be expressed as:

I(A) = −fA+ log2 fA+ − fA− log2 fA−

accessible vs inaccessible environment

If an agent's sensory apparatus gives it access to the complete state of the environment, then we say that the environment is accessible to that agent.إذا كان الجهاز الحسي للوكيل يمنحه الوصول إلى الحالة الكاملة للبيئة ، فإننا نقول أن البيئة يمكن الوصول إليها من قبل ذلك الوكيل.

Utility-based agents

If one state is preferred over the other, then it has higher utility for the agent Utility-Function (state) = real number (degree of happiness) The agent is aware of a utility function that estimates how close the current state is to the agent's goal

built in agent

If the agent's actions are based completely on built-in knowledgeإذا كانت إجراءات الوكيل مبنية بالكامل على المعرفة المدمجة

Static vs. dynamic

If the environment can change while an agent is deliberating, then we say the environment is dynamic for that agent; otherwise it is staticإذا كان من الممكن أن تتغير البيئة أثناء قيام الوكيل بالتداول ، فنحن نقول أن البيئة ديناميكية لذلك الوكيل. خلاف ذلك هو ثابت

Discrete vs. continuous.

If there are a limited number of distinct, clearly defined percepts and actions we say that the environment is discreteإذا كان هناك عدد محدود من المفاهيم والإجراءات المتميزة والمحددة بوضوح فإننا نقول أن البيئة منفصلة

Searle's claim is that, although it appears that the room understands Chinese, it cannot, since none of its components do—the man only understands English, and all the other elements are inanimate objects.

If we accept this, and substitute 'man' for 'computer', we see that a computer program that behaves as if it understands Chinese (and could thus pass the Turing test, at least with a Chinese interrogator) does not actually understand it.

Define Admissible heuristic h (n)

In A* search, if it is optimal then, h(n) is an admissible heuristic which means h(n) never overestimates the cost to reach the goal.

Episodic vs. nonepisodic

In an episodic environment, the agent's experience is divided into "episodes." Each episode consists of the agent perceiving and then actingفي بيئة عرضية ، تنقسم خبرة الوكيل إلى "حلقات". كل حلقة تتكون من عامل إدراك ومن ثم يتصرف

The Turing test

In the Turing test, an interrogator has a five minute conversation (via textual questions and answers) with two parties, one of which is a computer, and one of which is a human

Markov decision processes

In the simplest setting, a reinforcement learning agent's environment consists of a small number of distinct states

What is the structure of intelligent Agent?

Intelligent Agent = Architecture + Agent Program

Define Interpretation

Interpretation specifies exactly which objects, relations and functions are referred to by the constant predicate, and function symbols.

Define Depth first search.

It expands the deepest node in the current fringe of the search tree.

Define Greedy Best First Search.

It expands the node that is closest to the goal (i.e.) to reach solution in a quicker way. It is done by using the heuristic function: f(n) = h(n).

What is the meaning for greedy local search?

It goals (picks) a good neighbor state without thinking ahead about where to go next.

monotonicity.

It holds true for almost all admissible heuristics. A heuristic for which it holds is said to exhibit monotonicity.'

Define Hill Climbing search.

It is a loop that continually moves in a increasing value direction (i.e.) up hill and terminates when it reaches a "peak" where no neighbor has a higher value.

Define Agent Function.

It is a mathematical description which deals with the agent's behavior that maps the given percept sequence into an action.

Define Rational Agent

It is one that acts, so as to achieve the best outcome (or) when there is uncertainty, the best expected outcome

What is local search?

It operates using a single current state rather than multiple paths and generally moves only to neighbors of that state.

What is triangle inequality?

It states that each side of a triangle cannot be longer than the sum of the other two slides of the triangle

Learning agent has the following components

Learning element Suggests modification to the existing rule to the critic Performance element Collection of knowledge and procedures for selecting the driving actions Choice depends on Learning element Critic Observes the world and passes information to the learning element Problem generator Identifies certain areas of behavior needs improvement and suggest experiments

Utility is a measure of desirability of worlds to an agent

Let u(ω) be the utility of world ω to the agent. Simple goals can be specified by: worlds that satisfy the goal have utility 1; other worlds have utility 0. Often utilities are more complicated: for example some function of the amount of damage to a robot, how much energy is left, what goals are achieved, and how much time it has taken

List some drawbacks of hill climbing process

Local maxima: A local maxima as opposed to a goal maximum is a peak that is lower that the highest peak in the state space. Once a local maxima is reached the algorithm will halt even though the solution may be far from satisfactory. Plateaux: A plateaux is an area of the state space where the evaluation fn is essentially flat. The search will conduct a random walk

Inference

Logical inference is a process that implements the entailment relation between sentences

Characteristics of utility based agent

Looking for higher utility value utility function

. What Is Called As Decision Theory

Preferences As Expressed by Utilities Are Combined with Probabilities in the General Theory of Rational Decisions Called Decision Theory. Decision Theory = Probability Theory + Utility Theory

is modelled as a Markov Decision Process (MDP) described by a tuple:

M = hS, A, P,Ci, where S is the set of states that the agent may find itself in; A is a set of actions that the agent may execute; P(s 0 |s, a) is a transition function that describes the probability of moving to a state s 0 ∈ S from another state (s ∈ S) given an action (a ∈ A); and C(s, a) is a reward function that returns a scalar reward signal r for executing action a to state s.

Materialism, on the other hand

Materialism, on the other hand, holds that only material objects exist, so physical states are mental states: 'brains cause minds'.

Give the procedure of IDA* search.

Minimize f(n)=g(n)+h(n) combines the advantage of uniform cost search + greedy search A* is complete, optimal. Its space complexity is still prohibitive.

Context Free Grammars (CFG)

N a set of non-terminal symbols (or variables) • Σ a set of terminal symbols (disjoint from N) • R a set of productions or rules of the form A→β, where A is a non-terminal and β is a string of symbols from (Σ∪ N)* • S, a designated non-terminal called the start symbol

The effect of heuristic accuracy on performance

N= l+b* +(b*)2 + --- + (b~)d .

of objects, properties, relations, and functions

Objects: people, houses, numbers, theories, Ronald McDonald, colors, baseball games, wars, centuries ... • Relations: brother of, bigger than, inside, part of, has color, occurred after, owns ... • Properties: red, round, bogus, prime, multistoried... .-j» Functions: father of, best friend, third inning of, one more than ..

Define conditional probability

Once the agents has obtained some evidence concerning the previously unknown propositions making up the domain conditional or posterior probabilities with the notation p(A/B) is used. This is important that p(A/B) can only be used when all be is known

What is informed search?

One that uses problem - specific knowledge beyond the definition of the problem itself and it can find solutions more efficiently than an uninformed strategy.

Selecting attributes to split on

One useful measure of attribute usefulness is information gain

What is important for task environment?

PEAS → P- Performance measure E - Environment A- Actuators S - Sensors

what are path

Path: A path in the state space is a sequence of states connected by a sequence of actions

Define performance measures.

Performance measure embodies the criterion for success of an agent's behavior

How to measure the performance of an agent?

Performance measure of an agent is got by analyzing two tasks. They are How and When actions.

.What is the need for probability theory in uncertainty

Probability provides the way of summarizing the uncertainty that comes from our laziness and ignorance . Probability statements do not have quite the same kind of semantics known as evidences

Problem Formulation Process

Problem formulation is the process of deciding what actions and states to consider, and follows goal formulation

problem formulation

Problem formulation is the process of deciding what actions and states to consider, and follows goal formulation.

Define problem solving agent

Problem solving agent is one kind of goal based agent, where the agent Should select one action from sequence of actions which lead to desirable states

goal-based agent called a problem-solving agent

Problem-solving agents decide what to do by finding sequences of actions that lead to desirable statesتقرر عوامل حل المشكلات ما يجب القيام به من خلال إيجاد تسلسل من الإجراءات التي تؤدي إلى حالات مرغوبة

Meaning in propositional logic is context-independent

Propositional logic has very limited expressive power

What is the use of QUEUING_FN?

QUEUING_FN inserts asset of elements into the queue. Different varieties of queuing fn produce different varieties of the search algorithm.

Define Quantifier and it's types

Quantifiers are used to express properties of entire collection of objects rather than representing the objects by name.

Thinking rationally: The laws of thought approach

Rational thinking is the ability to think out of the box, to think critically, to produce "good quality of thoughts" in certain situations to arrive at a rational decisionالتفكير العقلاني هو القدرة على التفكير خارج الإطار ، والتفكير النقدي ، وإنتاج "أفكار جيدة" في مواقف معينة للوصول إلى قرار عقلاني.

Rationality

Rationality is in your self interest because the only way to achieve desired outcomes is to act according to reality. To understand reality, one must use reason consistently. Any deviation can have long term problems, since one's knowledge is often derived from one's previous knowledgeالعقلانية هي في مصلحتك الذاتية لأن الطريقة الوحيدة لتحقيق النتائج المرجوة هي العمل وفق الواقع. لفهم الواقع ، يجب على المرء استخدام العقل باستمرار. يمكن أن يكون لأي انحراف مشاكل طويلة الأمد ، حيث أن معرفة الفرد مستمدة في الغالب من المعرفة السابقة

Give example for real world end toy problems.

Real world problem examples: i. Airline travel problem. ii. Touring problem. iii. Traveling salesman problem. iv. VLSI Layout problem v. Robot navigation vi. Automatic Assembly vii. Internet searching Toy problem Examples: Vacuum world problem. 8 - Queen problem 8 - Puzzle problem

Four important classes of generative grammars are, in descending order of expressiveness:

Recursively enumerable grammars, which use rules that are not restricted on either side

Supervised learning

Regression, where the outputs that the system is trying to learn are continuous.

Regular grammars,

Regular grammars, which restrict the left-hand side (search pattern) to a single nonterminal, and the right-hand side (replacement pattern) to a single terminal symbol, optionally followed by a single symbol

first-order logic (like natural language) assumes the world contains

Relations: red, round, prime, brother of, bigger than, part of, comes between,

Roughly speaking, equivalent to the conjunction of instantiations of P

Richard the Lionheart is a king ⇒ Richard the Lionheart is a person ∧ King John is a king ⇒ King John is a person ∧ Richard's left leg is a king ⇒ Richard's left leg is a person ∧ John's left leg is a king ⇒ John's left leg is a person ∧ The crown is a king ⇒ The crown is a perso

Atomic sentences

Term = function (term1 ,...,termn ) or constant or variable

Simple CFG for ATIS English

S → NP VP S → Aux NP VP S → VP NP → Pronoun NP → Proper-Noun NP → Det Nominal Nominal → Noun Nominal → Nominal Noun Nominal → Nominal PP VP → Verb VP → Verb NP VP → VP PP PP → Prep N

What is SMA* search?

SMA* expands the best leaf until memory is full and it drops the oldest worst leaf node and expands the newest best leaf node.

Sentence Generation

Sentences are generated by recursively rewriting the start symbol using the productions until only terminals symbols remain.

Truth in first-order logic

Sentences are true with respect to a model and an interpretation

Define synchronic and diachronic sentence

Sentences dealing with same time are called synchronic sentences. Sentences that allow reasoning "a cross time" are called diachronic sentence.

What are the different types of problem?

Single state problem, multiple state problems, Contingency problem, Exploration problem

The 8-puzzIe

States: a state description specifies the location of each of the eight tiles in one of the nine squares. For efficiency, it is useful to include the location of the blank. Operators: blank moves left, right, up, or down. Goal test: state matches the goal configuration Path cost: each step costs 1, so the path cost is just the length of the path

non-terminal symbols

Symbols that are used to refer to another, more detailed syntax definition.

Define Syntax?

Syntax is the arrangement of words. Syntax of a knowledge describes the possible configurations that can constitute sentences. Syntax of the language describes how to make sentences.

WHAT is AI system ?

Systems that think like humans. Systems that think rationally Systems that act like humans. Systems that act rationally لأنظمة التي تفكر مثل البشر. الأنظمة التي تفكر بعقلانية الأنظمة التيا تعمل مثل البشر. الأنظمة التي تعمل بعقلانية

Simple Agents:

Table-driven agents: the function consists in a lookup table of actions to be taken for every possible state of the environment. If the environment has n variables, each with t possible states, then the table size is t n . Only works for a small number of possible states for the environment.

NATURE OF ENVIRONMENTS:

Task environment - the problem that the agent is a solution to. Includes Performance measure Environment Actuator Sensors

Chinese Room

The Chinese room appears to understand Chinese: it accepts pieces of paper with questions in Chinese through a slot, and a few minutes later outputs the answers, again written in Chinese, through another slot

Strong AI: can machines think?

The Strong AI claim goes one step further than the Weak AI claim. The Strong AI Claim is that a machine that behaves like a human has a mind and is capable of actually thinking, as opposed to simply carrying out a 'simulation' of thinking.

Contingency Problems. Contingency Problem

The agent doesn't know what effect its actions will have. This could be due to the environment being partially observable, or because of another agent. Ways to handle this: Solution is a list of conditional actions

Agent Function

The agent function is a mathematical function that maps a sequence of perceptions into action. The function is implemented as the agent program. The part of the agent taking an action is called an actuator. environment sensors agent function actuators environment

Rationality:

The agent's rational behavior depends on: the performance measure that defines success the agent's knowledge of the environment the action that it is capable of performing The current sequence of perceptions.

Define Optimization Problems.

The aim of this problem is to find the best state according to an objective function.

WHAT is AI?

The art of creating machines that perform functions that require intelligence when performed by people"فن إنشاء آلات تؤدي وظائف تتطلب ذكاء عندما يقوم بها الناس

WHAT is AI?

The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning أتمتة] الأنشطة التي نربطها بالتفكير الإنساني ، أنشطة مثل اتخاذ القرار ، حل المشكلات ، التعلم

What is called as principle of maximum expected utility

The basic idea is that an agent is rational if and only if it chooses the action that yields the highest expected utility, averaged over all the possible outcomes of the action. This is known as MEU

WHAT is AI?

The branch of computer science that is concerned with the automation of intelligent behavior"فرع علوم الكمبيوتر الذي يهتم بأتمتة السلوك الذكي

knowledge base, or KB

The central component of a knowledge-based agent knowledge base is a set of representations of facts about the world

Define Ontological commitment

The difference between propositional and first order logic is in the ontological commitment. It assumes about the nature of reality.

Define kinship domain

The domain of family relationship is called kinship domain which consists of objects unary predicate, binary predicate, function, relation.

Give example problems for Artificial Intelligence.

i. Toy problems ii. Real world problems

What is called as bidirectional search?

The idea behind bidirectional search is to simultaneously search both forward from the initial state & backward from the goal & stop when the two searches meet in the middle

Iterative Lengthening Search:

The idea is to use increasing path-cost limit instead of increasing depth limits. The resulting algorithm called iterative lengthening search

cognitive science

The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mindجمع مجال العلوم المعرفية متعدد التخصصات بين نماذج الكمبيوتر من الذكاء الاصطناعي والتقنيات التجريبية من علم النفس في محاولة لبناء نظريات دقيقة وقابلة للاختبار عن أساليب عمل العقل البشري

The interrogatoR Fnction

The interrogator must determine which is the human, and which is the machine; if they fail 30 per cent of the time the computer is said to have passed the test

knowledge-based agent at three levels

The knowledge level or epistemological level The logical level The implementation level

Define Epistemological commitment.

The logic that allows the possible states of knowledge with respect to each fact

Reinforcement learning is often primarily concerned with learning a value function

The most useful property of a value function is that it should have a zero Bellman error: Qπ(s, a) − (r + γQπ(s 0 , a 0 )) = 0,

Ambiguity between literal and figurative meanings.

The most common case of this is metonymy, where one object is used to stand for another.

The Chinese room

The most famous argument against the Strong AI claim is Searle's Chinese room.

Define branching factor (b).

The number of nodes which is connected to each of the node in search tree is called Branching factor

Define depth limited search

The problem of unbounded tress can be avoided by supplying depth limit 1(i.e.) nodes at depth 1 are treated as if they have no successors. This is called Depth Limited search.

BUILDING A KNOWLEDGE BASE

The process of building a knowledge base is called knowledge engineering

Knowledge Engineering

The process of designing rules, entering them into an expert system, and testing them.

Overfitting

The process of fitting a model too closely to the training data for the model to be effective on other data.

Define search node.

The root of the search tree that is the initial state of the problem is called search node

The syntax of a language describes the possible configurations that can constitute sentences

The semantics determines the facts in the world to which the sentences refer

Define Semantics

The semantics of the language defines the truth of each sentence with respect to each possible world. With this semantics, when a particular configuration exists with in an agent, the agent believes the corresponding sentence.

a knowledge representation language

The sentences are expressed in a language

Define State Space

The set of all possible states reachable from the initial state by any sequence of action is called state space.

. Define domain and domain elements

The set of objects is called domain, sometimes these objects are referred as domain elements.

Define Modus Ponen's rule in Propositional logic?

The standard patterns of inference that can be applied to derive chains of conclusions that lead to the desired goal is said to be Modus Ponen's rule.

This is equivalent to stating that there should be no difference between the value of a state-action pair and the reward it receives plus the value of the state-action pair that follows it (discounted by γ).

This is a consequence of our definition of return, and is known as a temporal difference error because it describes the error between the value function at two successive time steps

Advantages:Iterative Lengthening Search:

This method is preferred for large state space and when the depth of the search is not known. • Memory requirements are modest. • Like BFS it is complete Disadvantages: Many states are expanded multiple times.

Iterative Lengthening Search:

Time : N(IDS)=(d)b+(d-1)b2 +............+(1)bd - O(bd ) • Space - O(bd) • Optimal - Yes if step cost = 1 - Can be modified to explore uniform cost tree

Write the time & space complexity associated with depth limited search

Time complexity =O (bd) , b-branching factor, d-depth of tree Space complexity=o (bl)

What is meant by Turing test?

To conduct this test we need two people and one machine. One person will be an interrogator (i.e.) questioner, will be asking questions to one person and one machine. Three of them will be in a separate room. Interrogator knows them just as A and B. so it has to identify which is the person and machine.

TURING TEST

To pass the total Turing test, the computer will need, Computer vision: to perceive objects Robotics: to manipulate objects and move about

Turing claims

Turing claimed that any machine that passes such a test should be considered intelligent, and that it would one day be possible to build such a system.

Bidirectional Search

Two searches: one forward form the root and one backward form the goal. Goal test replaced with frontier intersect test. Requires a method of computing predecessors.

Define Quantifier and it's types

Types: i. Universal Quantifier ii. Existential Quantifier iii. Nested Quantifier.

Define Uniform cost search

Uniform cost search expands the node 'n' with the lowest path cost instead of expanding the shallowest node.

Define Branching factor b*

Uniform tree of depth d would have to be in order to contain N+1 nodes is called branching factor.

Agents that keep track of the world

Updating this internal state information as time goes by requires two kinds of knowledge to be encoded in the agent program. First, we need some information about how the world evolves independently of the agent—oيتطلب تحديث معلومات الحالة الداخلية هذه مع مرور الوقت نوعين من المعرفة التي يتم ترميزها في برنامج الوكيل. أولاً ، نحن بحاجة إلى بعض المعلومات حول كيفية تطور العالم بشكل مستقل عن العامل — س

Within AI, there has not been a big effort to try

Within AI, there has not been a big effort to try to pass the Turing test. The issue of acting like a human comes up primarily when AI programs have to interact with people, as when an expert system explains how it came to its diagnosis, or a natural language processing system has a dialogue with a user.داخل منظمة العفو الدولية ، لم يكن هناك جهد كبير لمحاولة اجتياز اختبار تورينج. تأتي قضية التصرف كإنسان في المقام الأول عندما تكون برامج الذكاء الاصطناعي متفاعلة مع الناس ، كما هو الحال عندما يشرح نظام خبير كيف وصل إلى تشخيصه ، أو أن نظام معالجة اللغة الطبيعية لديه حوار مع مستخدم.

knowledge base

a body of knowledge in a particular area that makes it easier to master new information in that area

Simple reflex agents

a connection a condition-action rule7 written as if car-in-front-is-bmking then initiate-brakingاتصال قاعدة تصرف - إجراء 7 مكتوبة كما لو أن car-in-front-is-bmking ثم تبدأ - braking

The ideal mapping from percept sequences to actions

a mapping from percept sequences to actionsرسم خريطة من تسلسلات التصور إلى أفعال

ach individual representation SENTENCE " is called

a sentence

Iterative deepening search

a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits: first depth 0, then depth 1, then depth 2, and so on

Reinforcement learning

a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance.

List the various search strategies

a. BFS b. Uniform cost search c. DFS d. Depth limited search e. Iterative deepening search f. Bidirectional search

The output of a search

algorithm is a solution, that is, a path from the initial state to a state that satisfies the goal test.

First-order logic has sentences

also has terms, which represent objects

INTELLIGENT AGENTS

an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics).إن العامل الذكي (IA) هو كيان مستقل يراقب من خلال أجهزة الاستشعار ويعمل على بيئة باستخدام المشغلات (أي أنه عامل) ويوجه نشاطه نحو تحقيق الأهداف (أي أنه "عقلاني" ، كما هو محدد في علم الاقتصاد).

The mathematical objection argues that, since there are some questions that are provably unanswerable by computers, and that since humans can presumably

answer them, a computer can never be as intelligent as a human. This argument relies on the assumption that humans are free from such limitations, which has not been shown to be the case. A related objection, the argument from informality, which states that computers are formal (mathematical) systems and human intelligence is not, and therefore cannot be modelled by such systems

Decision trees

are a relatively simple, popular method for classification when the input consists of variables (termed attributes) with a finite number of possible values (termed attribute values). Each node in a decision tree is either a decision or a tes

Decision variables

are like random variables that an agent gets to choose a value for

the grammar uses the | sign

as an "or" symbol: when the right-hand side contains A | B we may choose to replace the left-hand side with either A or B (but not both). We will use this grammar for the remainder of this chapter

goal-based agent

as well as a current state description,! the agent needs some sort of goal information, which describes situations that are desirable—j for example, being at the passenger's destinationفضلا عن وصف الحالة الحالية ،! يحتاج الوكيل إلى نوع ما من معلومات الأهداف ، التي تصف المواقف المرغوبة - على سبيل المثال ، كونها في وجهة الراكب

Model-based reflex agents

ated description of the current state. If the world is not fully observable, the agent must remember observations about the parts of the environment it cannot currently observe. This usually requires an internal representation of the world (or internal state). Since this representation is a model of the world, we call this model-based agent. Ex: Braking problem

Autonomous agent

autonomous to the extent that its behavior is determined b\ its own experience.إذا كانت إجراءات الوكيل مبنية بالكامل على المعرفة المدمجة

Depth-limited search

avoids the pitfalls of depth-first search by imposing a cutoff on the maximum depth of a path

The Turing test was a landmark thought experiment because it set out a clear, decisive target around which arguments for and against the possibility of building intelligent machines could be made. Moreover, such a system would need to be able to do a great deal, for example, it would need to:

be able to understand and generate sentences in English possess common sense about the world make inferences about the world from its common sense.

Syntactic Analysis

building a derivation tree with lexical tokens as its leaves. Any failure signals a grammatical error. (Partial syntactic and lexical analyses may interleave.)

syntactic parsing

building the syntactic structure of a sentence

Propositional logic

c is declarative

he systems argument, which says that although none of the individual components of the room understand Chinese, the room as a whole—as a system—does. Analogously, consider each of the neurons in a person's brain: in which neuron is the understanding of English? This boils down to the basic mind-body problem:

can a collection of simple things (atoms, neurons) be arranged in such a fashion as to result in a mind, even though none of them individually are one? According to the systems argument, the answer must be yes.

Autonomous agent

can adapt and alter the manner in which it worksيمكن تكييف وتغيير الطريقة التي يعمل بها

constraint satisfaction problem

can enormously reduce time complexity, but is not always applicable. Its memory requirements may be impractical.

Model-based reflex agents CHARACTERISTICS

characteristics Reflex agent with internal state Sensor does not provide the complete state of the world. must keep its internal state

Measuring Problem Solving Performance

completeness, optimality, time complexity, space complexity

Interpretation specifies referents fo

constant symbols → objects predicate symbols → relations function symbols → functional relations

simple planning agent

constructs plans that achieve its goals, and then executes them

terminal symbols

conventionally written in lowercase

The root of the search tree is a search node

corresponding to the initial state. The leaf nodes of the tree correspond to states that do not have successors in the tree,

sentence is satisfiable

if and only if there is some interpretation in some world for which it is true.

Agents that keep track of the world

for example, that an overtaking car generally will be closer behind than it was a moment ago. Second, we need some information about'how the agent's own actions affect the world—for example, that when the agent changes lanes to the right, there is a gap (at least temporarily) in the lane it was in before, على سبيل المثال ، أن سيارة متجاوزة بشكل عام ستكون أقرب إلى الخلف مما كانت عليه قبل لحظة. ثانياً ، نحن بحاجة إلى بعض المعلومات حول "كيف تؤثر تصرفات الوكيل على العالم" ، على سبيل المثال ، أنه عندما يتغير العامل إلى اليمين ، فإن هناك فجوة (على الأقل مؤقتًا) في الممر الذي كان عليه في السابق ،

types of agents in artificial intelligence

four types of agent program: • Simple reflex agents • Agents that keep track of the world • Goal-based agents • Utility-based agents أربعة أنواع من برنامج الوكيل: • عوامل منعكس بسيطة • الوكلاء الذين يتتبعون العالم • العوامل القائمة على الهدف • العوامل القائمة على الأدوات المساعدة

skeleton of an agent

function SKELETON-agent [(precept) returns action static: memory, the agent's memory of the world memory — UPDATE-MEMORY(memory, precept ) action <— CHOOSE-BEST-ACTION(memory ) memory — UPDATE-MEMORY(memory , action) return actionلدالة SKELETON-agent [(precept) يرجع العمل الاستاتيكي: الذاكرة ، ذاكرة الوكيل للذاكرة العالمية - UPDATE-MEMORY (الذاكرة ، المبدأ) الإجراء <- CHOOSE-BEST-ACTION (الذاكرة) الذاكرة - UPDATE-MEMORY (الذاكرة ، العمل ) العودة العمل

simple reflex agent algorithm

function SiMPLE-REFLEX-AGENT(precept) returns action static: rules, a set of condition-action rules state <— lNTERpRET-lNPUT(precept ) rtf/e <- RULE-MATCH(states, rules) action <- RULE-action [rule] return actionالدالة SiMPLE-REFLEX-AGENT (precept) تقوم بإرجاع الإجراء الثابت: القواعد ، مجموعة من إجراءات الإجراء - الحالة <- lnterpRET-lNPUT (precept) rtf / e <- RULE-MATCH (حالات ، قواعد) الإجراء <- RULE-action [حكم] عودة الإجراء

two candidatesh

h2 = the sum of the distances of the tiles from their goal positions. Because tiles cannot move along diagonals, the distance we will count is the sum of the horizontal and vertical distances. This is sometimes called the city block distance or Manhattan distance. h2 is also admissible, because any move can only move one tile one step closer to the goal. The 8 tiles in the start state give a Manhattan distance of h2 = 2 + 3 + 2 + 1+2 + 2+ 1+2 =15

background knowledge.

he agent maintains a knowledge base, KB, which may initially contain some

advantage of goal based agent

he goal-based agent is also more flexible with respect to reaching different destinations. Simply by specifying a new destination, we can get the goal-based agent to come up with a new behaviorوكيل المرتكزة على المرونة هو أيضا أكثر مرونة فيما يتعلق بالوصول إلى وجهات مختلفة. ببساطة عن طريق تحديد وجهة جديدة ، يمكننا أن نحصل على وكيل المرتكز على الهدف ليخرج بسلوك جديد

What are the components of a problem?

here are four components. They are i. initial state ii. Successor function iii. Goal test iv. Path cost v. Operator vi. state space vii. path

performance measure

how—the criteria that determine how successful an agent isكيف - المعايير التي تحدد مدى نجاح الوكيل

e straight-line distance to the goal

hsLo(n )= straight-line distance between n and the goal location

What are the 3 types of symbol which is used to indicate objects, relations and functions?

i) Constant symbols for objects ii) Predicate symbols for relations iii) Function symbols for functions

List the performance measures of search strategies

i. Completeness ii. Optimality iii. Time complexity iv. Space complexity

What are the 2 types of synchronic rules?

i. Diagnostic rules ii. Casual rules.

List the steps involved in simple problem solving technique

i. Goal formulation ii. Problem formulation iii. Search iv. Solution v. Execution phase

What are the basic Components of propositional logic?

i. Logical Constants (True, False)

What are the 2 ways to use all available memory?

i. Memory bounded A*(MA*) ii. Simplified Memory bounded A*(SMA*)

What are the 2 types of memory bounded heuristic algorithms?

i. Recursive Best First Search(RBFS) ii. Memory bounded A*(MA*)

What are the variants of hill climbing?

i. Stochastic hill climbing ii. First choice hill climbing iii. Simulated annealing search iv. Local beam search v. Stochastic beam searc

List the properties of environments

o Fully Observable Vs Partially Observable o Deterministic Vs Stochastic o Episodic Vs Sequential o Static Vs Dynamic o Discrete Vs Continuous o Single Agent Vs Multi agent a. Competitive Multi agent b.Co - operative Multi agen

Atomic sentences

referring to objects, and predicate symbols for referring to relations, we can put them togetHef to make atomic sentenctssTfiaT state" facts

The implementation level

s the level that runs on the agent architecture. It is the level at which there are physical representations of the sentences at the logical level.

search tree

search process as building up a search tree

Information measures the amount of information required in a set of examples for predicting the label. For example, if A is the whole set of examples, then we see that there are 14 examples, seven of which are positive and seven of which are negative

so fA+ = 7 14, and similarly fA− = 7 14. Then I(A) = − 7 14 log2 7 14 − 7 14 log2 7 14 = −log2 1 2 = 1. Notice that if all of the examples have the same label then the information is zero: we need zero information to determine the labels of the data.

4-Static vs. Dynamic

static AI environments rely on data-knowledge sources that don't change frequently over time. Speech analysis is a problem that operates on static AI environments. Contrasting with that model, dynamic AI environments such as the vision AI systems in drones deal with data sources that change quite frequently.تعتمد بيئات الذكاء الاصطناعي الثابتة على مصادر معرفة البيانات التي لا تتغير بشكل متكرر مع مرور الوقت. تحليل الكلام هو مشكلة تعمل على بيئات الذكاء الاصطناعي الثابتة. وعلى النقيض من هذا النموذج ، تتعامل بيئات الذكاء الاصطناعي الديناميكية مثل أنظمة الرؤية البصرية في الطائرات بدون طيار مع مصادر البيانات التي تتغير بشكل متكرر.

Equality

term1 = term2 is true under a given interpretation if and only if term1 and term2 refer to the same object

This allows us to model tasks

that the agent can perform repeatedly in order to improve its performance

a lexicon

that the grammar is separated into a lexicon,

conclusion on agent

the agent mapping as a function from percept sequences to actions, the agent program receives only a single percept as its input. It is up to the agent to build up the percept sequence in memory, if it so desiresتعيين وكيل كدالة من تسلسلات الحسي إلى الإجراءات ، يتلقى برنامج الوكيل فقط إدراك واحد كمدخله. والأمر متروك للوكيل لبناء تسلسل إدراكي في الذاكرة ، إذا رغب في ذلك

basics of problem

the basic elements of a problem definition are the states and actions

Deterministic vs. nondeterministic

the environment is completely determined by the current state and the actions selected by the agents, then we say the environment is deterministic.يتم تحديد البيئة بالكامل من خلال الحالة الحالية والإجراءات التي اختارها الوكلاء ، ثم نقول أن البيئة حتمية.

WHAT is AI?

the exciting new effort to make computers think . . . machines with minds, in the full and literal sense الجهد الجديد المثير لجعل أجهزة الكمبيوتر تفكر. . . الآلات مع العقول ، بالمعنى الكامل والحرفي

the goal performance of an agent

the goal or performance measure is not part of the skeleton program. This is because the performance measure is applied externally to judge the behavior of the agentالهدف أو قياس الأداء ليس جزءًا من برنامج الهيكل العظمي. ويرجع ذلك إلى تطبيق مقياس الأداء خارجياً للحكم على سلوك الوكيل

Determining what follows from what the KB has been TELLed is the job of the inference mechanism,

the other main component of a knowledge-based agent

rational agent i

the problem of deciding how and when to evaluate the agent's success.مشكلة تحديد كيفية وموعد تقييم نجاح الوكيل

An important property of MDPs is the Markov property

which is that for any state s, all the information the agent needs to make a decision about what to do next is contained in s. This means, for example, that we do not need to worry about the agent's history.

Context-free grammars

which restrict the left-hand side (search pattern) to a single non-terminal symbol. We call this grammar context-free because each rule allows the replacement of a symbol no matter what surrounds it. For example, the context-free rule A → CxD specifies that the non-terminal symbol A is replaced by the string CxD; this can occur without regard to the string before or after A

pathmax equation

will always be non decreasing along any path from the root, provided h is admissible

the resulting strategy is called best-first search

with GENERAL-SEARCH

With an example, show objects, properties functions and relations.

xample "EVIL KING JOHN BROTHER OF RICHARD RULED ENGLAND IN 1200" Objects : John, Richard, England, 1200 Relation : Ruled Properties : Evil, King Functions : BROTHER OF

RELATION WITH OTHER DISCIPLINES:

xpert Systems - Natural Language Processor - Speech Recognition - Robotics - Computer Vision - Intelligent Computer-Aided Instruction - Data Mining - Genetic Algorithms

Expand one node to the depth of the tree. If dead end occurs, backtracking is done to the next immediate previous node for the nodes to be expanded

• Expand the deepest unexpanded node • Unexplored successors are placed on a stack until fully explored • Enqueue nodes on nodes in LIFO (last-in, first-out) order. That is, nodes used as a stack data structure to order nodes. • It has modest memory requirement. • It needs to store only a single path from the root to a leaf node, along with remaining unexpanded sibling nodes for each node on a path • Back track uses less memory.

Explanation:

• On holiday in Romania; currently in Arad • Flight leaves tomorrow from Bucharest • Formulate goal: - be in Bucharest • Formulate problem: - states: various cities - actions: drive between cities • Find solution: - sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest

A Problem solving Agent, Assuming the environment is

• Static • Observable • Discrete • Deterministic

what is rational at any given time depends on four things:

• The performance measure that defines degree of success. • Everything that the agent has perceived so far. We will call this complete perceptual history the percept sequence. • What the agent knows about the environment. • The actions that the agent can perform• مقياس الأداء الذي يحدد درجة النجاح. • كل شيء ينظر إليه الوكيل حتى الآن. سنطلق على هذا التاريخ الإدراكي الكامل التسلسل المدرك. • ما يعرف الوكيل عن البيئة. • الإجراءات التي يمكن للوكيل القيام بها

two candidates

• h\ = the number of tiles that are in the wrong position. For Figure 4.7, none of the 8 tiles is in the goal position, so the start state would have h\ = 8. h\ is an admissible heuristic, because it is clear that any tile that is out of place must be moved at least once.

goal test

•, which the agent can apply to a single state description to determine if it is a goal state

All Kings are persons: ∀x King(x) ⇒ Person(x)

∀x P is true in a model m iff P is true with x being each possible object in the model