Learning Generalized Policies in Planning Using Concept Languages

In this paper we are concerned with the problem of learning how to solve planning problems in one domain given a number of solved instances. This problem is formulated as the problem of inferring a function that operates over all instances in the domain and maps states and goals into actions. We call such functions generalized policies and the question that we address is how to learn suitable representations of generalized policies from data. This question has been addressed recently by Roni Khardon 16]. Khardon represents generalized policies using an ordered list of existentially quantiied rules that are inferred from a training set using a version of Rivest's learning algorithm 22]. Here, we follow Khardon's approach but represent generalized policies in a diierent way using a concept language. We show through a number of experiments in the blocks-world that the concept language yields a better policy using a smaller set of examples and no background knowledge. The policy representation is related to the indexical-functional representations advocated by Agre and Chap-man 1] and domain concepts such as`the-next-needed-block' andà-well-placed-block' are identiied from scratch.