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This paper presents a general neurosymbolic reasoning and learning methodology that modularly integrates Answer Set Programming with an Energy Based Model substrate, supporting joint optimization in continuous latent space and end-to-end training. It demonstrates applications on MNIST, CLEVR, and MOT benchmarks.
This paper presents a novel fuzzy-logic-based extension of Answer Set Programming (ASP) that integrates numerical information with qualitative reasoning using membership functions, demonstrated through a travel recommendation case study.
This paper accelerates the NeurASP neurosymbolic AI framework by implementing vectorization, batch processing, and caching, achieving multiple orders of magnitude speedup on larger tasks.
This paper proposes MONIR, a Modalized-Output Normative Intermediate Representation that bridges LLM-assisted norm extraction and ASP-based compliance reasoning for technical standards. The framework is instantiated on Chinese ADAS regulations, combining symbolic reasoning with LLM pipelines for explainable compliance checking.
This paper presents a method for distilling answer-set programming rules from large language models to enhance neurosymbolic visual question answering, showing that only a few examples are needed to generate correct rules.
This paper presents an Answer Set Programming (ASP) based implementation of the CARCASS framework for constructing abstractions in reinforcement learning, demonstrating its effectiveness on Blocks World and Minigrid domains.