Abstract
Many of today’s most successful planners perform a forward heuristic search. The accuracy of the heuristic estimates and the cost of their computation determine the performance of the planner. Thanks to the efforts of researchers in the area of heuristic search planning, modern algorithms are able to generate high-quality estimates. In this paper we propose to learn heuristic functions using artificial neural networks and support vector machines. This approach can be used to learn standalone heuristic functions but also to improve standard planning heuristics. One of the most famous and successful variants for heuristic search planning is used by the Fast-Forward (FF) planner. We analyze the performance of standalone learned heuristics based on nature-inspired machine learning techniques and employ a comparison to the standard FF heuristic and other heuristic learning approaches. In the conducted experiments artificial neural networks and support vector machines were able to produce standalone heuristics of superior accuracy. Also, the resulting heuristics are computationally much more performant than related ones.
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This work was supported by a fellowship within the Postdoc-Program of the German Academic Exchange Service (DAAD).
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Satzger, B., Kramer, O. Goal distance estimation for automated planning using neural networks and support vector machines. Nat Comput 12, 87–100 (2013). https://doi.org/10.1007/s11047-012-9332-y
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DOI: https://doi.org/10.1007/s11047-012-9332-y