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Ch. 2 – Intelligent Agents

Learn about the ideal rational agent that always maximizes performance, given its percepts, and how Table-Driven Agents function using percept sequences. Explore the assignment operations and variables involved.

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Ch. 2 – Intelligent Agents

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

  2. Agent Agent 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 percepts sensors Environment ? actions actuators

  3. Table Driven Agent function TABLE-DRIVEN-AGENT(percept)returns an action static: 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. 45

  4. Table Driven Agent function name input output type function TABLE-DRIVEN-AGENT(percept)returns an action static: 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. 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

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