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

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.

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

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

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)> …. …

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