Ch. 2 – Intelligent Agents

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

2. Agent Agent 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. percepts sensors Environment ? actions actuators

3. Table Driven Agent functionTable-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)returnaction From Figure 2.7, p. 47

4. Table Driven Agent function name input output type functionTable-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)returnaction 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, Scissors, Paper Table Driven Agent

6. Goal-Based 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