Both goalbased and utilitybased agents have goals However, having goals isn't effective (or efficient) enough, given that a goalbased agent may have several actions that can lead to the goals, but not all these actions are equally effective So there's the need for an agent to perform the most effective action And this is done by a utilityGoal information describes situations that are desirable This provides the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state Search and planning are the subfields of artificial intelligence devoted to finding action sequences that achieve the agent's goals Utilitybased agents• Goalbased agent Goalbased agents are modelbased agents which sorts goal information that describes situations • Utilitybased agent This is an agent that uses an explicit utility function that maximizes the expected utility • Learning agent This is an agent that improves its behavior based on its experiences and learning ...
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