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Explore how table-driven agents make decisions based on percept sequences, maximizing performance with built-in knowledge and percepts from the environment. Learn the functions and benefits of table-driven agents in intelligent systems.
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Ch. 2 – Intelligent Agents Supplemental slides for CSE 327 Prof. Jeff Heflin
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
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. 47
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. 47 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
