Ch. 2 – Intelligent Agents

Supplemental slides for CSE 327Prof. Jeff Heflin

Agent

Environment

sensors

actuators

?

Agent

percepts

actions

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.

function TABLE-DRIVEN-AGENT(percept)returns an action

persistent: percepts, a sequence, initially emptytable, a table of actions, indexed by

percept sequences

append percept to the end of perceptsaction LOOKUP(percepts, table)return action

From Figure 2.7, p. 47

Table Driven Agent

function TABLE-DRIVEN-AGENT (percept)returns an action

persistent: percepts, a sequence, initially emptytable, a table of actions, indexed by percept sequences

append percept to the end of perceptsaction LOOKUP(percepts, table)return action

From Figure 2.7, p. 47

Table Driven Agentfunction

name inputoutput type

persistent variables: maintain values between function calls, like instance variables in OO, but can only be referenced within the function

assignment operation

output value

function call

Rock, Scissors, Paper Table Driven Agent

Percept Sequence Action

<Start> Rock

<Start, Win(Rock,Scissors)> Rock

<Start, Lose(Rock,Paper)> Scissors

<Start, Tie(Rock,Rock)> Paper

<Start, Win(Rock,Scissors), Win (Rock,Scissors)> Rock

<Start, Win(Rock,Scissors), Lose(Rock,Paper)> Scissors

<Start, Win(Rock,Scissors), Tie(Rock,Rock)> Paper

<Start, Lose(Rock,Paper), Win(Scissors,Paper)> Scissors

<Start, Lose(Rock,Paper), Lose(Scissors,Rock)> Paper

<Start, Lose(Rock,Paper), Tie(Scissors,Scissors)> Rock

…. …

Goal-Based Agent

sensors

actuatorsAgent

Environment

What the world is like now

What action I should do nowGoals

State

How the world evolves

What my actions doWhat it will be like if I do action A

From Fig. 2.13, p. 52