#logic-programming News & Analysis

5 articles tagged with #logic-programming. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AINeutralarXiv – CS AI · 15h ago5/10

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2-ASP(Q) programs with weak constraints: Complexity and efficient implementation

Researchers present 2-ASP(Q)^w, a fragment of Answer Set Programming extended with quantifiers and weak constraints, proving its theoretical complexity bounds and introducing practical computation strategies using CEGAR techniques. The work bridges theoretical computer science with implementable solutions for optimization problems, offering both formal completeness results and experimental validation on real-world benchmarks.

AINeutralarXiv – CS AI · May 125/10

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Weighted Rules under the Stable Model Semantics

Researchers introduce weighted rules under stable model semantics, combining logic programming with probabilistic methods similar to Markov Logic Networks. This advancement enables answer set programs to handle inconsistencies, rank solutions, assign probabilities, and perform statistical inference—moving beyond the deterministic limitations of traditional logic-based systems.

AINeutralarXiv – CS AI · May 125/10

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Functional Stable Model Semantics and Answer Set Programming Modulo Theories

Researchers demonstrate how functional stable model semantics enhances Answer Set Programming Modulo Theories (ASPMT), enabling integration of intensional functions that derive values from other predicates rather than pre-defined sources. The framework allows tight ASPMT programs to translate into SMT instances, extending the theoretical foundations of logic programming.

AINeutralarXiv – CS AI · May 125/10

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Cplus2ASP: Computing Action Language C+ in Answer Set Programming

Cplus2ASP Version 2 is a new system that translates action language C+ into answer set programming, offering significant performance improvements over the Causal Calculator through modern ASP solving techniques. The tool supports incremental execution, external atoms via Lua integration, and extensible translations for other action languages, making it relevant for automated reasoning and planning applications.

AINeutralarXiv – CS AI · Apr 106/10

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Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach

Researchers propose using Inductive Learning of Answer Set Programs (ILASP) to create interpretable approximations of neural networks trained on preference learning tasks. The approach combines dimensionality reduction through Principal Component Analysis with logic-based explanations, addressing the challenge of explaining black-box AI models while maintaining computational efficiency.