Principles of artificial intelligence
Principles of artificial intelligence
Learning representations by back-propagating errors
Neurocomputing: foundations of research
The computational complexity of propositional STRIPS planning
Artificial Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Using Automated Planning for Trusted Self-organising Organic Computing Systems
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
Weighted A∗ search -- unifying view and application
Artificial Intelligence
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The first learning track of the international planning competition
Machine Learning
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
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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.