Utility based agents



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 perfsnapshot 1orm.

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 a re 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.





Sources: (Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Novig), Reference »