restaurant-1.0

数据集restaurant-1.0由迈阿密大学工程与计算学院的研究人员创建，包含40个餐厅故事，旨在为研究典型人类活动提供资源，并作为自然语言处理、自然语言理解、知识表示推理和答案集编程社区的有用基准。该数据集涵盖了多种餐厅场景，包括正常、意外和诊断场景，通过这些故事可以评估和改进使用ASP理论意图理解叙事的方法。数据集中的每个故事都附有ASP逻辑形式，便于进行深入的逻辑分析和推理。

The dataset restaurant-1.0 was created by researchers from the College of Engineering and Computing, University of Miami, and contains 40 restaurant stories. It aims to provide resources for the study of typical human activities, and serves as a valuable benchmark for the communities of natural language processing (NLP), natural language understanding (NLU), knowledge representation and reasoning, and answer set programming (ASP). This dataset covers a variety of restaurant scenarios including normal, unexpected and diagnostic ones, through which methods that utilize ASP theory to understand narrative intentions can be evaluated and improved. Each story in the dataset is accompanied by an ASP logical form, facilitating in-depth logical analysis and reasoning.