Toward Discriminative Learning of Planning Heuristics
نویسندگان
چکیده
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 beamsearch 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.
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