Chapter 2 - Intelligent Agents
Percept Sequence
Complete history of everything the agent has ever perceived
Deterministic vs Non-deterministic
Deterministic - When the next state of the environment is completely determined by the current state and the action executed by the agent Non-deterministic - subsequent states not completely determined by current state and agent's actions ( most real complex situations - traffic is unpredictable )
Discrete vs Continuous
Discrete - finite number of states and discrete set of percepts and actions Continuous - continuous state and continuous time problems, actions are continuous (taxi driving - steering angle)
Static vs Dynamic
Dynamic - the environment can change while an agent is deliberating (Taxi-driving) Static - environment is constant while agent is deliberating (Crossword) Semidynamic - When the env is constant but the agent's performance score changes over time (chess when played with a clock) Static env's are easier for agents (less time pressure), and in dynamic indecision (dithering) is a decision to do nothing.
Circular flow of perceiving and acting:
Env -> Percepts -> Sensors -> Agent -> Actuators -> Actions -> Env
Episodic vs Sequential
Episodic - Agent's experience divided into atomic episodes, wherein each episode the agent receives a percept and performs a single action, and that subsequent episodes do not depend on the actions taken in previous episodes ( assembly line ) Sequential - Current decisions can effect future decisions; short-term actions can have long-term consequences (chess) Episodic simpler than Sequential as Agent does not need to think ahead
Rationality Criterion in a sentence
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.
Fully Observable vs Partially Observable
Fully - agent's sensors give access to complete state of environment; sensors detect all aspects that are relevant to the choice of action; relevance depends on performance measure Partially - Noisy & Inaccurate sensors, parts of state are missing from sensor data (short range sensors) Unobservable - No Sensors at all
Percept
Content an agent's sensors are perceiving
Task Environment Example: Image Analysis
1) Fully Observable 2) Single-Agent 3) Deterministic 4) Episodic 5) Semi 6) Continuous
Example of Vacuum Percept Sequence
(A, Clean) - Right, (A, Dirty) - Suck, (B, clean) - Left, (B, dirty) - Suck, (A, clean) ; (A, clean) - Right ... etc.
Task Environment Example: Chess with a clock
1) Fully Observable 2) Multi-agent 3) Deterministic 4) Sequential 5) Semi 6) Discrete
Task Environment Example: Crossword Puzzle
1) Fully Observable 2) Single Agent 3) Deterministic 4) Sequential 5) Static 6) Discrete
Task Environment Example: Poker
1) Partially Observable 2) Multi-Agent 3) Stochastic 4) Sequential 5) Static 6) Discrete
Agent
Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Performance Measure - Rationality criteria #1
Defines Criterion for success
Single-agent vs Multi-agent
Entities that are agents - Entities behaviour that maximize a performance measure whose value depends on another agent's behaviour are agents Single - One agent (crossword puzzle) Multi - many agents (chess, opponent is Agent)
Rationality is the same as Omniscience (T/F)
False
Rational Agents must ___ information and ___ as much as possible from what it perceives
Gather, Learn
Known vs Unknown
Known - Agent's (or designer's) state of knowledge about the laws of physics of the environment - the outcomes for all actions are given Unknown - agent has to learn how the environment works in order to make good decisions
Omniscient Agent
Knows Actual Outcome of actions
Information gathering
Part of rationality and means doing actions in order to modify future percepts
Intelligent Agent
Receive Agents and perform actions
Sequence of Actions by the agent leads to ...
Sequence of States for the environment
Rest of Chapter 2
Wednesday Content
Rationality maximizes ___ performances, while perfection maximizes ___ performance
expected, actual
A rational agent is one that does
the right thing
Task Environment Example: Taxi Driving
1) Partially Observable 2) Multi-Agent 3) Stochastics 4) Sequential 5) Dynamic 6) Continuous
Task Environment Example: Medical Diagnosis
1) Partially Observable 2) Single-Agent 3) Stochastic 4) Sequential 5) Dynamic 6) Continuous
Four Criterion for Rationality
1) Performance Measure 2) Prior knowledge of environment 3) Actions an agent can perform 4) Percept sequence to date
Task Environment Dimensions and Properties
Observable - fully vs partially Agent - single vs multi Deterministic - Deter. vs non-deter. (stochastic) Episodic - Sequential vs Episodic Static - Dynamic vs static vs semi Discrete - Continuous vs Discrete
PEAS Description of Taxi Driver
Performance Measure - safe, fast, legal Environment - roads, traffic, police Actuators - Steering, accelerator, brake Sensors - speedometer, gps, radar
Task Environment Specifications (PEAS - stands for?)
Performance measure, Environment, Actuators, Sensors
An agent Lacks Autonomy if and only if
the Agent relies on the designer's percepts