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2004

Learning Generalized Policies from Planning Examples Using Concept Languages

11 years 8 months ago
Learning Generalized Policies from Planning Examples 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 (Technical Report TR-09-97, Harvard, 1997). Khardon represents generalized policies using an ordered list of existentially quantified rules that are inferred from a training set using a version of Rivest's learning algorithm (Machine Learning, vol. 2, no. 3, pp. 229
Mario Martin, Hector Geffner
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Where APIN
Authors Mario Martin, Hector Geffner
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