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

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Presentation on theme: "Ch. 2 – Intelligent Agents Supplemental slides for CSE 327 Prof. Jeff Heflin."— Presentation transcript:

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

2 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.

3 function T ABLE -D RIVEN -A GENT (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  L OOKUP (percepts, table) return action From Figure 2.7, p. 47 Table Driven Agent

4 function T ABLE -D RIVEN -A GENT (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  L OOKUP (percepts, table) return action From Figure 2.7, p. 47 Table Driven Agent function name input output 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

5 Rock, Scissors, Paper Table Driven Agent Percept SequenceAction Rock Rock Scissors Paper Rock Scissors Paper Scissors Paper Rock ….…

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


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