artificial intelligence chapter 2

a model of the world

An agent that uses such a model is called a model-based agent

fully observable vs partially observable

An agent's sensors give it access to the complete state of the environment at each point in time.

a 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.

Static vs. dynamic

If the environment can change while an agent is deliberating, then DYNAMIC we say the environment is dynamic for that agent; otherwise, it is static.

Deterministic vs. stochastic.

If the next state of the environment is completely deterSTOCHASTIC mined by the current state and the action executed by the agent, then we say the environment is deterministi

Episodic vs. sequential:

In an episodic task environment, the agent's experience is SEQUENTIAL divided into atomic episodes In each episode the agent receives a percept and then performs a single action.

Goal-based agents

Knowing something about the current state of the environment is not always enough to decide what to do

peas

Performance, Environment, Actuators, Sensors

intelligent systems

Simple reflex agents; • Model-based reflex agents; • Goal-based agents; and • Utility-based agents

Known vs. unknown

Strictly speaking, this distinction refers not to the environment UNKNOWN itself but to the agent's (or designer's) state of knowledge about the "laws of physics" of the environment.

Discrete vs. continuous

The discrete/continuous distinction applies to the state of the CONTINUOUS environment, to the way time is handled, and to the percepts and actions of the agent

agent

agent = architecture + program

an agent relies on the prior knowledge of its designer rather than AUTONOMY on its own percepts,

agent lacks autonomy

the agent function for an artificial agent will be implemented

agent program

Simple reflex agents

agents select actions on the basis SIMPLE REFLEX AGENT of the current percept, ignoring the rest of the percept history.

Single agent vs. multiagent:

an agent solving a crossword puzzle by itself is clearly in a single-agent environment, whereas an agent playing chess is in a twoagent environment.

An agent

anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

rational agents

as central to our approach to artificial intelligence

Static environments are easy to deal with

because the agent need not keep looking at the world while it is deciding on an action, nor need it worry about the passage of time

type of cheese action

chess is a competitive multiagent environment

Taxi driving is a continuous-state

continuous-time problem: the speed and location of the taxi and of the other vehicles sweep through a range of continuous values and do so smoothly over time

In sequential environments, on the other hand, the current decision

could affect all future decisions.

In many areas of AI, this is now the preferred method

d for creating state-of-the-art systems.

the behavior of a rational agent can become

effectively independent of its prior knowledge.

a performance measure

evaluates any given sequence of environment states

agent maximize

hen an agent that chooses actions to maximize its utility will be rational according to the external performance measure

If the environment is partially observable

hen it could appear to be stochastic

a condition-action rule

if car-in-front-is-braking then initiate-braking

fully observable

if the sensors detect all aspects that are relevant to the choice of action; relevance, in turn, depends on the performance measure

an agent need not worry about uncertainty

in a fully observable, deterministic environment

Chess and taxi driving are sequential

in both cases, short-term actions can have long-term consequences.

Task Environment

includes entities that directly affect a firm on a constant basis and include competitors, suppliers, and customers

utility function

is essentially an internalization of the performance measure

The most effective way to handle partial observability

is for the agent to keep track of the part of the world it can't see now

agent's percept sequence

is the complete history of everything the agent has ever perceived

A rational agent should be autonomous—

it should learn what it can to compensate for partial or incorrect prior knowledge

A software agent receives

keystrokes, file contents, and network packets as sensory inputs and acts on the environment by displaying on the screen, writing files, and sending network packets.

the goal-based agent appears

less efficient, it is more flexible because the knowledge that supports its decisions is represented explicitly and can be modified.

. Rationality

maximizes expected performance, while perfection maximizes actual performance.

A robotic agent

might have cameras and infrared range finders for sensors and various motors for actuators.

The agent function

n is an abstract mathematical description; the agent program is a concrete implementation, running within some physical system

designing an agent, the first step

o specify the task environment as fully as possible

information gathering

s an important part of rationality

If the environment itself does not change with the passage of time but the agent's performance score does

semidynamic

A utility-based agent has to model and keep track of its environment

tasks that have involved a great deal of research on perception, representation, reasoning, and learning

agent function

that maps any given percept sequence to an action

An omniscient agent

the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality

goal information agent

the agent needs some sort of goal information that describes situations that are desirable

Agent programs

they take the current percept as input from the sensors and return an action to the actuators.

the term percept

to refer to the agent's perceptual inputs at any given instant

Utility-based agents

try to maximize their own expected "happiness"

If the agent has no sensors at all then the environment

unobservUNOBSERVABLE able

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

• The performance measure that defines the criterion of success. • The agent's prior knowledge of the environment. • The actions that the agent can perform. • The agent's percept sequence to date.