Abstract
Macro search is used to derive solutions quickly for large search spaces at the expense of optimality. We present a novel way of building macro tables. Our contribution is twofold: (1) for the first time, we use automatically generated heuristics to find optimal macros, (2) due to the speed-up achieved by (1), we merge consecutive subgoals to reduce the solution lengths.We use the Rubik's Cube to demonstrate our techniques. For this puzzle, a 44% improvement of the average solution length was achieved over macro tables built with previous techniques.
I like to thank Dr. Robert C. Holte and Dr. Jonathan Schaeffer for helpful comments. This research was partially founded by an NSERC grant.
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Hernádvölgyi, I.T. (2001). Searching for Nacro Operators with Automatically Generated Heuristics. In: Stroulia, E., Matwin, S. (eds) Advances in Artificial Intelligence. Canadian AI 2001. Lecture Notes in Computer Science(), vol 2056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45153-6_19
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DOI: https://doi.org/10.1007/3-540-45153-6_19
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