In this paper, we propose ProRAC (Progression-based Reasoning about Actions and Change), a neuro-symbolic framework that leverages LLMs to tackle RAC problems. ProRAC extracts fundamental RAC elements including actions and questions from the problem, progressively executes each action to derive the final state, and then evaluates the query against the progressed state to arrive at an answer. We evaluate ProRAC on several RAC benchmarks, and the results demonstrate that our approach achieves strong performance across different benchmarks, domains, LLM backbones, and types of RAC tasks.

翻译：本文提出ProRAC（基于递进的行动与变化推理），一种利用大语言模型处理RAC问题的神经符号框架。ProRAC从问题中提取包括行动和提问在内的基本RAC要素，逐步执行每个行动以推导最终状态，随后根据递进状态评估查询以得出答案。我们在多个RAC基准测试上评估ProRAC，结果表明该方法在不同基准、领域、LLM骨干网络及RAC任务类型中均表现出强劲性能。