Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912089091604480 |
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| author | Chen, Dillon Z. Thiébaux, Sylvie |
| author_facet | Chen, Dillon Z. Thiébaux, Sylvie |
| contents | Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning Chen, Dillon Z. Thiébaux, Sylvie Artificial Intelligence Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics. |
| title | Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2404.05235 |