Multi-valued Logics (Computer and Information Science)

多值逻辑（计算机与信息科学）

• 批准号: 8620059
• 负责人: Nils Nilsson
• 金额: $ 23.34万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-09-15 至 1990-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8620059&HistoricalAwards=false
• 关键词: Multi; valued; Logics; Computer; Information

This research explores the theoretical and computational properties of a formal system that subsumes many of the inference mechanisms currently of interest in artificial intelligence research and applications. The theoretical work proposed involves developing a uniform framework of which various formal extensions to first order logic are special cases. The extensions we will consider include default reasoning, probabilistic inference, and reason maintenance systems. The research will enable us to recognize computationally effective (or ineffective) inference schemes in advance. We also intend to describe in a precise way the calculational compromises made by production systems such as MYCIN in implementing such extensions. Finally, we propose to implement a general-purpose inference engine incorporating the theory developed and capable of being conveniently specialized to perform inference suitable for any of a variety of domains. The significance of this project is that it will provide a bridge for artificial intelligence research between theoretical work on the nature of reasoning and practical work in developing programs such as expert systems. The benefits will include not only new theoretical insights but also new algorithms and tools which will allow more flexible development of expert systems, and improve the performance of such systems when they have been completed.

本研究探讨了一个形式系统的理论和计算特性，该系统包含了人工智能研究和应用中当前感兴趣的许多推理机制。提出的理论工作包括发展一个统一的框架，其中对一阶逻辑的各种形式扩展是特殊情况。我们将考虑的扩展包括默认推理、概率推理和推理维护系统。这项研究将使我们能够提前识别计算上有效（或无效）的推理方案。我们还打算以精确的方式描述生产系统（如MYCIN）在实现此类扩展时所做的计算妥协。最后，我们建议实现一个通用的推理引擎，结合已开发的理论，并能够方便地专门执行适用于任何各种领域的推理。该项目的意义在于，它将为人工智能研究在推理本质的理论工作和开发专家系统等程序的实际工作之间提供一座桥梁。其好处不仅包括新的理论见解，还包括新的算法和工具，这将允许更灵活地开发专家系统，并提高这些系统完成后的性能。