Feldman, J.

Decision theory and artificial intelligence II: The hungry monkey


This paper describes a problem-solving framework in which aspects of mathematical decision theory are incorporated into symbolic problem-solving techniques currently predominant in artificial intelligence. The utility function of decision theory is used to reveal tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility function on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed.

Decision theory and artificial intelligence I: Semantics-based region analyzer


Mathematical decision theory can be combined with heuristic techniques to attack Artificial Intelligence problems. As a first example, the problem of breaking an image into meaningful regions is considered. Bayesian decision theory is seen to provide a mechanism for including problem dependent (semantic) information in a general system. Some results are presented which make the computation feasible.