Goal-Directed Learning: A Decision-Theoretic Model for Deciding What to Learn Next

This paper describes a theory called Goal-Directed Learning (gdl) that uses the principle of decision theory to choose learning tasks. The expected utility of being able to predict various features of the environment is computed and those with highest expected utility can be used as learning goals, which an agent's inductive mechanism should form theories to predict. We present a general decision-theoretic formula for the utility of learning goals, formalizing the concept that the best learning goals are those which, if learned, would maximize the agent's expected utility. The performance element of pagoda (Probabilistic Autonomous GOal-Directed Agent), an autonomous agent design presented in (desJardins 1992), is described , and a formula is given for computing the utility of learning goals in pagoda.