Tag
该论文提出一个基于回答集编程(ASP)的混合声明式框架，用于对交互式运动轨迹进行可解释的前瞻性推理，并在Argoverse 2基准上进行评估，每个预测轨迹都附带可检查的事件级假设。
A survey paper analyzing over 50 neurosymbolic AI systems, comparing Datalog, answer set programming, and probabilistic logic programs across semantics, expressiveness, neural integration, and evaluation, with a decision matrix and open-problem roadmap for choosing rule-based languages.
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.