Planning with Minimal Disruption
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913014387572736 |
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| author | Pozanco, Alberto Morales, Marianela Borrajo, Daniel Veloso, Manuela |
| author_facet | Pozanco, Alberto Morales, Marianela Borrajo, Daniel Veloso, Manuela |
| contents | In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Planning with Minimal Disruption Pozanco, Alberto Morales, Marianela Borrajo, Daniel Veloso, Manuela Artificial Intelligence In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives. |
| title | Planning with Minimal Disruption |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.15358 |