Learning generalized plans using abstract counting

Authors:
Siddharth Srivastava;Neil Immerman;Shlomo Zilberstein
Affiliations:
Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Year:
2008

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Abstract

Given the complexity of planning, it is often beneficial to create plans that work for a wide class of problems. This facilitates reuse of existing plans for different instances drawn from the same problem or from an infinite family of similar problems. We define a class of such planning problems called generalized planning problems and present a novel approach for transforming classical plans into generalized plans. These algorithm-like plans include loops and work for problem instances having varying numbers of objects that must be manipulated to reach the goal. Our approach takes as input a classical plan for a certain problem instance. It outputs a generalized plan along with a classification of the problem instances where it is guaranteed to work. We illustrate the utility of our approach through results of a working implementation on various practical examples.