Briefly describe utility -based agents
|Subject||Artificial Intelligence and Neural Network|
|NU Year||Set: 1.(c) Marks: 3+5=8 Year: 2010|
Just having goals isn’t good enough because often we may have several actions which all satisfy our goal so we need some way of working out the most efficient one. A utility function maps each state after each action to a real number representing how efficiently each action achieves the goal. This is useful when we either have many actions all solving the same goal or when we have many goals that can be satisfied and we need to choose an action to perform. For example, let’s show our mars Lander on the surface of Mars with an obstacle in its way. In a goal based agent, it is uncertain which path will be taken by the agent and some are clearly not as efficient as others but in a utility-based agent the best path will have the best output from the utility function and that path will be chosen.
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