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This special issue is dedicated to John McCarthy, founding father of Artificial Intelligence. It contains a collection of recent contributions to the field of knowledge representation and reasoning, a field that McCarthy founded and that has been a main focus of his research during the last half century. In this introductory article, we survey some of McCarthy's major contributions to the field of knowledge representation and reasoning, and situate the papers in this special issue in the context of McCarthy's previous work. 相似文献
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基于数据库的知识表达与推理研究 总被引:4,自引:0,他引:4
本文研究了专家系统的知识表达和推理机制,提出了基于数据库的知识表达方式。这种方式使数据库具有了知识库的功能,使知识更容易表达和管理。同时,提出了满足这种表达方式的推理机制,提高了专家系统的推理效率。 相似文献
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Tzachi Rosen Solomon Eyal Shimony Eugene Santos Jr. 《Annals of Mathematics and Artificial Intelligence》2004,40(3-4):403-425
Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extends the well-known Bayes Networks (BN), by naturally allowing for context-specific independence and for cycles in the directed graph. We present a semantics for BKBs that facilitate handling of marginal probabilities, as well as finding most probable explanations. Complexity of reasoning with BKBs is NP hard, as for Bayes networks, but in addition, deciding consistency is also NP-hard. In special cases that problem does not occur. Computation of marginal probabilities in BKBs is another hard problem, hence approximation algorithms are necessary – stochastic sampling being a commonly used scheme. Good performance requires importance sampling, a method that works for BKBs with cycles is developed. 相似文献
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Richard W. Weyhrauch Marco Cadoli Carolyn L. Talcott 《Journal of Logic, Language and Information》1998,7(1):77-101
Many formalisms for reasoning about knowing commit an agent to be logically omniscient. Logical omniscience is an unrealistic principle for us to use to build a real-world agent, since it commits the agent to knowing infinitely many things. A number of formalizations of knowledge have been developed that do not ascribe logical omniscience to agents. With few exceptions, these approaches are modifications of the possible-worlds semantics. In this paper we use a combination of several general techniques for building non-omniscient reasoners. First we provide for the explicit representation of notions such as problems, solutions, and problem solving activities, notions which are usually left implicit in the discussions of autonomous agents. A second technique is to take explicitly into account the notion of resource when we formalize reasoning principles. We use the notion of resource to describe interesting principles of reasoning that are used for ascribing knowledge to agents. For us, resources are abstract objects. We make extensive use of ordering and inaccessibility relations on resources, but we do not find it necessary to define a metric. Using principles about resources without using a metric is one of the strengths of our approach.We describe the architecture of a reasoner, built from a finite number of components, who solves a puzzle, involving reasoning about knowing, by explicitly using the notion of resource. Our approach allows the use of axioms about belief ordinarily used in problem solving – such as axiom K of modal logic – without being forced to attribute logical omniscience to any agent. In particular we address the issue of how we can use resource-unbounded (e.g., logically omniscient) reasoning to attribute knowledge to others without introducing contradictions. We do this by showing how omniscient reasoning can be introduced as a conservative extension over resource-bounded reasoning. 相似文献
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产生式系统的关系数据库实现 总被引:2,自引:1,他引:1
产生式系统是一种广为使用的知识表示方法,但随着知识库规模的不断增大,其知识的组织与管理越来越困难,从而直接影响系统的推理效率,本文采用关系数据库技术,对产生式系统进行改进,从此提高大规模产生式系统的运行效率。 相似文献
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In this paper we describe a computational architecture for applications that support heterogeneous reasoning. Heterogeneous reasoning is, in its most general form, reasoning that employs representations drawn from multiple representational forms. Of particular importance, and the principal focus of the architecture, is heterogeneous reasoning which employs one or more forms of graphical representation, perhaps in combination with sentences (of English or another language, whether natural or scientific). Graphical representations include diagrams, pictures, layouts, blueprints, flowcharts, graphs, maps, tables, spreadsheets, animations, video, and 3D models. By ‘an application that supports heterogeneous reasoning’ we mean an application that allows users to construct, record, edit, and replay a process of reasoning using multiple representations so that the structure of the reasoning is maintained and the informational dependencies and justifications of the individual steps of the reasoning can be recorded. 相似文献
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In this paper we provide a foundation of a theory of contextual reasoning from the perspective of a theory of knowledge representation. Starting from the so-called metaphor of the box, we firstly show that the mechanisms of contextual reasoning proposed in the literature can be classified into three general forms (called localized reasoning, push and pop, and shifting). Secondly, we provide a justification of this classification, by showing that each mechanism corresponds to operating on a fundamental dimension along which context dependent representations may vary (namely, partiality, approximation and perspective). From the previous analysis, we distill two general principles of a logic of contextual reasoning. Finally, we show that these two principles can be adequately formalized in the framework of MultiContext Systems. In the last part of the paper, we provide a practical illustration of the ideas discussed in the paper by formalising a simple scenario, called the Magic Box problem. 相似文献
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针对当前智能教学系统存在的不足,设计了一个基于WEB的智能教学系统模型。主要论述了系统的功能结构,学生模型和教师模型的构造以及知识表示方法和推理机运用知识进行推理的过程,侧重于方法与策略的研究与应用。旨在通过本系统的实现,为网络学习中的用户提供个性化、智能化的学习内容与指导。 相似文献
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常识知识的研究与发展得到了人工智能界的很大重视。文章建立了一个基于常识的人物亲属关系推理模型,研究了亲属关系常识以及人物信息的表示与存储。此外,对实际所要解决的问题进行了总结。 相似文献
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回顾跨媒体智能的发展历程,分析跨媒体智能的新趋势与现实瓶颈,展望跨媒体智能的未来前景。跨媒体智能旨在融合多来源、多模态数据,并试图利用不同媒体数据间的关系进行高层次语义理解与逻辑推理。现有跨媒体算法主要遵循了单媒体表达到多媒体融合的范式,其中特征学习与逻辑推理两个过程相对割裂,无法综合多源多层次的语义信息以获得统一特征,阻碍了推理和学习过程的相互促进和修正。这类范式缺乏显式知识积累与多级结构理解的过程,同时限制了模型可信度与鲁棒性。在这样的背景下,本文转向一种新的智能表达方式——视觉知识。以视觉知识驱动的跨媒体智能具有多层次建模和知识推理的特点,并易于进行视觉操作与重建。本文介绍了视觉知识的3个基本要素,即视觉概念、视觉关系和视觉推理,并对每个要素展开详细讨论与分析。视觉知识有助于实现数据与知识驱动的统一框架,学习可归因可溯源的结构化表达,推动跨媒体知识关联与智能推理。视觉知识具有强大的知识抽象表达能力和多重知识互补能力,为跨媒体智能进化提供了新的有力支点。 相似文献
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To appropriately utilize the rapidly growing amount of data and information is a big challenge for people and organizations. Standard information retrieval methods, using sequential processing combined with syntax-based indexing and access methods, have not been able to adequately handle this problem. We are currently investigating a different approach, based on a combination of massive parallel processing with case-based (memory-based) reasoning methods. Given the problems of purely syntax-based retrieval methods, we suggest ways of incorporating general domain knowledge into memory-based reasoning. Our approach is related to the properties of the parallel processing microchip MS160, particularly targeted at fast information retrieval from very large data sets. Within this framework different memory-based methods are studied, differing in the type and representation of cases, and in the way that the retrieval methods are supported by explicit general domain knowledge. Cases can be explicitly stored information retrieval episodes, virtually stored abstractions linked to document records, or merely the document records themselves. General domain knowledge can be a multi-relational semantic network, a set of term dependencies and relevances, or compiled into a global similarity metric. This paper presents the general framework, discusses the core issues involved, and describes three different methods illustrated by examples from the domain of medical diagnosis. 相似文献