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Answer Set Programming enhanced with new semantics and LLM-driven optimization · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore advancements in Answer Set Programming (ASP). The first paper introduces a unified logical framework, Bound-Founded Semantics, to characterize various semantics for ASP extensions with linear constraints, specifically focusing on clingo[DL]. The second paper adapts the StreamLLM approach, using large language models to generate 'streamliners' that optimize ASP encodings by pruning the solution space, achieving significant speedups on benchmark problems. AI

IMPACT These advancements in Answer Set Programming could lead to more efficient problem-solving in areas like constraint satisfaction and logic-based AI systems.

RANK_REASON Two academic papers published on arXiv detailing new theoretical frameworks and practical optimization techniques for Answer Set Programming.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Answer Set Programming enhanced with new semantics and LLM-driven optimization · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Pedro Cabalar (University of A Corunna, Spain), Jorge Fandinno (University of Nebraska at Omaha, USA), Nicolas R\"uhling (University of Potsdam, Germany), Torsten Schaub (University of Potsdam, Germany,Potassco Solutions, Germany), Sebastian Schellhorn (… ·

    Bound-Founded Semantics for Answer Set Programming with Difference Constraints: Preliminary Report

    arXiv:2607.21201v1 Announce Type: new Abstract: While the integration of linear constraints has significantly expanded the reach of Answer Set Programming (ASP), existing hybrid solvers often rely on disparate semantic underpinnings that lack a unified logical foundation. We addr…

  2. arXiv cs.AI TIER_1 Dansk(DA) · Florentina Voboril (TU Wien), Martin Gebser (University of Klagenfurt), Stefan Szeider (TU Wien), Alice Tarzariol (University of Klagenfurt) ·

    Streamliners for Answer Set Programming

    arXiv:2604.19251v2 Announce Type: replace-cross Abstract: Streamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space. We adapt the StreamLLM approach, which uses Large Language Models (LLMs) to generate streamliners for …

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