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B. Satzger, O. Kramer:
"Goal distance estimation for automated planning using neural networks and support vector machines";
Natural Computing, Volume 12 (2013), Issue 1; S. 87 - 100.

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.

Planning, Artificial neural network, Support vector machine, Search heuristic, Regression

http://dx.doi.org/10.1007/s11047-012-9332-y