A framework for Conditional Reasoning in Answer Set Programming
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908750414086144 |
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| author | Alviano, Mario Giordano, Laura Dupré, Daniele Theseider |
| author_facet | Alviano, Mario Giordano, Laura Dupré, Daniele Theseider |
| contents | In this paper we introduce a Conditional Answer Set Programming framework (Conditional ASP) for the definition of conditional extensions of Answer Set Programming (ASP). The approach builds on a conditional logic with typicality, and on the combination of a conditional knowledge base with an ASP program, and allows for conditional reasoning over the answer sets of the program. The formalism relies on a multi-preferential semantics, and on the KLM preferential semantics, as a special case. Conditional entailment is encoded in ASP and a complexity upper-bound is provided. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_03997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A framework for Conditional Reasoning in Answer Set Programming Alviano, Mario Giordano, Laura Dupré, Daniele Theseider Artificial Intelligence Logic in Computer Science In this paper we introduce a Conditional Answer Set Programming framework (Conditional ASP) for the definition of conditional extensions of Answer Set Programming (ASP). The approach builds on a conditional logic with typicality, and on the combination of a conditional knowledge base with an ASP program, and allows for conditional reasoning over the answer sets of the program. The formalism relies on a multi-preferential semantics, and on the KLM preferential semantics, as a special case. Conditional entailment is encoded in ASP and a complexity upper-bound is provided. |
| title | A framework for Conditional Reasoning in Answer Set Programming |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2506.03997 |