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Home / Proceedings / Proceedings of the International Symposium on Combinatorial Search

Optimal Search with Neural Networks: Challenges and Approaches

February 1, 2023

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Authors

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner

University of Alberta, Canada,University of Alberta, Canada,University of Alberta, Canada,Department of Computing Science, Alberta Machine Intelligence Institute (Amii), University of Alberta, Canada,Ben-Gurion University, Israel,Ben-Gurion University, Israel


DOI:

10.1609/socs.v15i1.21758


Abstract:

Work in machine learning has grown tremendously in the past years, but has had little to no impact on optimal search approaches. This paper looks at challenges in using deep learning as a part of optimal search, including what is feasible using current public frameworks, and what barriers exist for further adoption. The primary contribution of the paper is to show how to learn admissible heuristics through supervised learning from an existing heuristic. Several approaches are described, with the most successful approach being based on learning a heuristic as a classifier and then adjusting the quantile used with the classifier to ensure heuristic admissibility, which is required for optimal solutions. A secondary contribution is a description of the Batch A* algorithm, which can batch evaluations for more efficient use by the GPU. While ANNs can effectively learn heuristics that produce smaller search trees than alternate compression approaches, there still exists a time overhead when compared to efficient C++ implementations. This point of evaluation points out a challenge for future work.

Topics: SOCS

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HOW TO CITE:

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner Optimal Search with Neural Networks: Challenges and Approaches Proceedings of the International Symposium on Combinatorial Search (2022) 109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner Optimal Search with Neural Networks: Challenges and Approaches SOCS 2022, 109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner (2022). Optimal Search with Neural Networks: Challenges and Approaches. Proceedings of the International Symposium on Combinatorial Search, 109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner. Optimal Search with Neural Networks: Challenges and Approaches. Proceedings of the International Symposium on Combinatorial Search 2022 p.109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner. 2022. Optimal Search with Neural Networks: Challenges and Approaches. "Proceedings of the International Symposium on Combinatorial Search". 109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner. (2022) "Optimal Search with Neural Networks: Challenges and Approaches", Proceedings of the International Symposium on Combinatorial Search, p.109-117

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner, "Optimal Search with Neural Networks: Challenges and Approaches", SOCS, p.109-117, 2022.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner. "Optimal Search with Neural Networks: Challenges and Approaches". Proceedings of the International Symposium on Combinatorial Search, 2022, p.109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner. "Optimal Search with Neural Networks: Challenges and Approaches". Proceedings of the International Symposium on Combinatorial Search, (2022): 109-117.

Tianhua Li,Ruimin Chen,Borislav Mavrin,Nathan R. Sturtevant,Doron Nadav,Ariel Felner. Optimal Search with Neural Networks: Challenges and Approaches. SOCS[Internet]. 2022[cited 2023]; 109-117.


ISSN: 2832-9163


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