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Explore the concepts of rational agents and goal-based strategies for maximizing performance in varying environments. Learn about Table-Driven Agents and their function to select optimal actions based on percept sequences. Delve into the interplay between percepts, actions, and the environment, as well as the use of persistent variables to maintain crucial values. Gain insights into how sensors inform an agent of the present state and how actuators execute actions to achieve objectives.
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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 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
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
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
