1. Ch. 2 – Intelligent Agents
Supplemental slides for CSE 327
Prof. Jeff Heflin

2. Agent Review
Agent
percepts
sensors
Environment ?
actions
actuators
ideal rational agent: an agent that always takes the action expected to maximize its performance measure, given the set of percepts (percept sequence) it has seen so far

3. Table Driven Agent
function TABLE-DRIVEN-AGENT(percept) returns an action
static: percepts, a sequence, initially empty table, a table of actions, indexed by percept sequences append percept to the end of percepts action  LOOKUP(percepts, table) return action
From Figure 2.7, p. 45

4. Table Driven Agent function name input output type
function TABLE-DRIVEN-AGENT(percept) returns an action
static: percepts, a sequence, initially empty table, a table of actions, indexed by percept sequences append percept to the end of percepts action  LOOKUP(percepts, table) return action
From Figure 2.7, p. 45
assignment operation
function call
output value
static variables: maintain values between function calls, like instance variables in OO, but can only be referenced within the function

5. Rock, Scissors, Paper Table Driven Agent
Percept Sequence
Action
<Start>
Rock
<Start, Win(Rock,Scissors)>
<Start, Lose(Rock,Paper)>
Scissors
<Start, Tie(Rock,Rock)>
Paper
<Start, Win(Rock,Scissors), Win (Rock,Scissors)>
<Start, Win(Rock,Scissors), Lose(Rock,Paper)>
<Start, Win(Rock,Scissors), Tie(Rock,Rock)>
<Start, Lose(Rock,Paper), Win(Scissors,Paper)>
<Start, Lose(Rock,Paper), Lose(Scissors,Rock)>
<Start, Lose(Rock,Paper), Tie(Scissors,Scissors)> …. …