Defeasible Conditionals using Answer Set Programming
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866915713030029312 |
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| author | Dennison, Racquel Heyninck, Jesse Meyer, Thomas |
| author_facet | Dennison, Racquel Heyninck, Jesse Meyer, Thomas |
| contents | Defeasible entailment is concerned with drawing plausible conclusions from incomplete information. A foundational framework for modelling defeasible entailment is the KLM framework. Introduced by Kraus, Lehmann, and Magidor, the KLM framework outlines several key properties for defeasible entailment. One of the most prominent algorithms within this framework is Rational Closure (RC). This paper presents a declarative definition for computing RC using Answer Set Programming (ASP). Our approach enables the automatic construction of the minimal ranked model from a given knowledge base and supports entailment checking for specified queries. We formally prove the correctness of our ASP encoding and conduct empirical evaluations to compare the performance of our implementation with that of existing imperative implementations, specifically the InfOCF solver. The results demonstrate that our ASP-based approach adheres to RC's theoretical foundations and offers improved computational efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_03840 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Defeasible Conditionals using Answer Set Programming Dennison, Racquel Heyninck, Jesse Meyer, Thomas Artificial Intelligence Logic in Computer Science Defeasible entailment is concerned with drawing plausible conclusions from incomplete information. A foundational framework for modelling defeasible entailment is the KLM framework. Introduced by Kraus, Lehmann, and Magidor, the KLM framework outlines several key properties for defeasible entailment. One of the most prominent algorithms within this framework is Rational Closure (RC). This paper presents a declarative definition for computing RC using Answer Set Programming (ASP). Our approach enables the automatic construction of the minimal ranked model from a given knowledge base and supports entailment checking for specified queries. We formally prove the correctness of our ASP encoding and conduct empirical evaluations to compare the performance of our implementation with that of existing imperative implementations, specifically the InfOCF solver. The results demonstrate that our ASP-based approach adheres to RC's theoretical foundations and offers improved computational efficiency. |
| title | Defeasible Conditionals using Answer Set Programming |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2601.03840 |