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

2. Agent
Agent
percepts
sensors
Environment ?
actions
actuators
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.

3. Table Driven Agent
function Table-Driven-Agent(percept) returns an action
persistent: 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. 47

4. Table Driven Agent function name input output type
function Table-Driven-Agent (percept) returns an action
persistent: 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. 47
assignment operation
function call
output value
persistent variables: maintain values between function calls, like instance variables in OO, but can only be referenced within the function

5. Rock, Paper, Scissors 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)> …. …

6. Goal-Based Agent Environment Agent sensors State
What the world is like now
How the world evolves
Environment
What it will be like if I do action A
What my actions do
What action I should do now
Goals
actuators
Agent
From Fig. 2.13, p. 52