Modular and taxonomic representations for tractable reasoning about actions

用于易于处理的动作推理的模块化和分类表示

• 批准号: 262120-2009
• 负责人: Soutchanski, Mikhail
• 金额: $ 1.38万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=480164
• 关键词: Modular; taxonomic; representations; tractable; reasoning

A long standing and fundamental problem of AI research is this: the development of a comprehensive and general theory for representation and efficient reasoning about actions of agents operating in uncertain and incompletely known large-scale domains. The proposed research will contribute towards this long-term goal by eliciting structured logical representations and subsequently elaborating them to guarantee efficient reasoning in structured domains. The proposed research focuses on representations of actions formulated in order-sorted first order logic and in description logic-based languages. The primary objective of the proposed research program is the theoretical development of those representations of actions and their effects that can be characterized as modular, taxonomic or structural and the experimental assessment of these representations on solving the common reasoning tasks. This research is original and innovative for several reasons. First, it emphasizes the development of logical theories of actions that exploit the declarative structure of application domains: taxonomies of actions and objects. Second, it proposes to integrate taxonomic reasoning about actions and about objects with order-sorted logics and modular description logic ontologies. Finally, it proposes to use these representational leverages to solving challenging and important problems in AI, including decision-theoretic planning and learning. While the proposed research program will help to advance our knowledge, it also has a significant promise of developing new logical frameworks, information technologies (and subsequently, software products) that will be relevant for a broad range of applications including health-care, Semantic Web services and industry.

人工智能研究的一个长期存在的基本问题是：发展一个全面和通用的理论，用于表示和有效推理在不确定和不完全已知的大规模领域中操作的代理的行为。拟议的研究将有助于实现这一长期目标，引出结构化的逻辑表示，并随后详细说明它们，以保证在结构化域的有效推理。建议的研究重点是表示顺序排序的一阶逻辑和描述逻辑为基础的语言制定的行动。拟议研究计划的主要目标是理论发展那些可描述为模块化、分类学或结构化的动作及其效果的表示，以及这些表示在解决常见推理任务方面的实验评估。这项研究具有原创性和创新性，原因有几个。首先，它强调开发的逻辑理论的行动，利用应用程序域的声明性结构：动作和对象的分类法。 其次，它提出了集成分类推理的行动和对象的顺序排序逻辑和模块化描述逻辑本体。最后，它建议使用这些代表性的杠杆来解决人工智能中具有挑战性和重要性的问题，包括决策理论规划和学习。虽然拟议的研究计划将有助于推进我们的知识，它也有一个显着的承诺，开发新的逻辑框架，信息技术（以及随后的软件产品），将相关的广泛的应用，包括医疗保健，语义Web服务和行业。