Toward Discriminative Learning of Planning Heuristics

Yuehua Xu, Alan Fern

We consider the problem of learning heuristics for controlling forward state-space search in AI planning domain. We draw on a recent framework for structured output classification (e.g. syntactic parsing) known as learning as search optimization LaSO). The LaSO approach uses discriminative learning to optimize heuristic functions for search-based computation of structured outputs and has shown promising results in a number of domains. However, the search problems that arise in AI planning tend to be qualitatively very different from those considered in structured classification, which raises a number of potential difficulties in directly applying LaSO to planning. In this paper, we discuss these issues and describe a LaSO-based approach for discriminative learning of beam-search heuristics in AI planning domains. Our preliminary results in three benchmark domains are promising. In particular, across a range of beam-sizes the discriminatively trained heuristic outperforms the one used by the planner FF and another recent non-discriminative learning approach.

Subjects: 1.11 Planning; 12. Machine Learning and Discovery

Submitted: May 22, 2006

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