AINeutralarXiv – CS AI · Jun 256/10
🧠Researchers present a hybrid answer set programming method for computing constrained movement trajectories of autonomous objects in real-world environments. The approach combines logical reasoning with geometric constraints to generate interpretable trajectory modes, demonstrated on autonomous driving datasets with verifiable explainability advantages over purely learned approaches.
AINeutralarXiv – CS AI · Jun 235/10
🧠Researchers introduce a joint air traffic flow and capacity management model using Answer Set Programming that simultaneously optimizes aircraft trajectories and sector configurations. The ASP approach outperforms traditional Mixed Integer Programming methods and remains competitive with heuristics, demonstrating potential improvements in balancing flight demand with available airspace capacity.
AINeutralarXiv – CS AI · Jun 116/10
🧠This arXiv survey examines explainable AI (XAI) methods applied to Answer Set Programming (ASP), a symbolic AI approach used for declarative reasoning. The paper catalogs existing explanation approaches and tools while identifying gaps in coverage across different user scenarios, establishing a foundation for future XAI research in logic-based systems.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers have significantly improved NeurASP, a neurosymbolic AI framework that combines neural networks with symbolic reasoning, through vectorization, batch processing, and caching techniques. The enhancements achieve speedups of multiple orders of magnitude, addressing previous computational bottlenecks that limited scalability for complex tasks.
AINeutralarXiv – CS AI · Jun 45/10
🧠Researchers propose MONIR, a normative intermediate representation framework for automated compliance reasoning using Answer Set Programming (ASP). The system combines staged operational semantics with executable ASP compilation to evaluate regulatory adherence, demonstrated through application to Chinese ADAS (Advanced Driver Assistance Systems) regulations with LLM-assisted extraction pipelines.
AINeutralarXiv – CS AI · Jun 15/10
🧠Researchers have developed an Answer-Set Programming (ASP) based implementation of the CARCASS framework to improve Reinforcement Learning abstractions for complex state spaces. The approach leverages ASP's declarative modeling capabilities as an alternative to Prolog, demonstrating promising results in Blocks World and Minigrid domains when domain knowledge is available.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers propose a meta-programming framework that enables flexible implementation of temporal logic extensions for Answer Set Programming (ASP) through a unified declarative system. The work introduces metasp, a tool that allows rapid exploration of different temporal logics—including linear-time (TEL), metric (MEL), and dynamic (DEL) variants—without modifying core ASP system code.
AINeutralarXiv – CS AI · May 275/10
🧠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
🧠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
🧠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
🧠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.
AIBullisharXiv – CS AI · May 16/10
🧠Researchers present LLM+ASP, a framework combining large language models with Answer Set Programming to enable nonmonotonic reasoning without task-specific engineering. The system uses automated self-correction loops where an ASP solver provides structured feedback, demonstrating significant performance improvements over monotonic logic approaches across diverse reasoning benchmarks.
AINeutralarXiv – CS AI · Apr 106/10
🧠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.
