A semantic characterization of a useful fragment of the situation calculus with knowledge

The situation calculus, as proposed by McCarthy and Hayes, and developed over the last decade by Reiter and co-workers, is reconsidered. A new logical variant called ES is proposed that captures much of the expressive power of the original, but where certain technical results are much more easily proved. This is illustrated using two existing non-trivial results: the determinacy of knowledge theorem of Reiter and the regression theorem, which reduces reasoning about the future to reasoning about the initial situation. Furthermore, we show the correctness of our approach by embedding ES in Reiter's situation calculus.     

A unifying action calculus

McCarthy's Situation Calculus is arguably the oldest special-purpose knowledge representation formalism, designed to axiomatize knowledge of actions and their effects. Four decades of research in this area have led to a variety of alternative formalisms: While some approaches can be considered instances or extensions of the classical Situation Calculus, like Reiter's successor state axioms or the Fluent Calculus, there are also special planning languages like ADL and approaches based on a linear (rather than branching) time structure like the Event Calculus. The co-existence of many different calculi has two main disadvantages: The formal relations among them is a largely open issue, and a lot of today's research concerns the transfer of specific results from one approach to another. In this paper, we present a unifying action calculus, which encompasses (well-defined classes of) all of the aforementioned formalisms. Our calculus not only facilitates comparisons and translations between specific approaches, it also allows to solve interesting problems for various calculi at once. We exemplify this by providing a general, calculus-independent solution to a problem of practical relevance, which is intimately related to McCarthy's quest for elaboration tolerant formalisms: the modularity of domain axiomatizations.     

A framework for building knowledge-bases under uncertainty

Managing uncertainty during the knowledge engineering process from elicitation to validation and verification requires a flexible, intuitive, and semantically sound knowledge representation. This is especially important since this process is typically highly interactive with the human user to add, update, and maintain knowledge. In this paper, we present a model of knowledge representation called Bayesian Knowledge-Bases (BKBs). It unifies a ‘if-then’ style rules with probability theory. We also consider the computational efficiency of reasoning over BKBs. We can show that through careful construction of the knowledge-base, reasoning is computationally tractable and can in fact be polynomial-time. BKBs are currently fielded in the PESKI intelligent system development environment.     

应急系统中不确定时态知识表示及其推理

在分析现有不确定信息表示模型基础上,提出了一种可扩展的时态知识表示模型,该模型对真实事件的间断性表示加以改进,进而讨论了应急决策知识库的构建方法和相应推理机制。实验数据表明,在原型系统中使用改进后的表示模型和推理方法,能够提高应急系统处理效率和决策方案的合理性。     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

Neural Networks and Structured Knowledge: Rule Extraction and Applications

As the second part of a special issue on Neural Networks and Structured Knowledge, the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of structured knowledge by neural networks. The transformation of the low-level internal representation in a neural network into higher-level knowledge or information that can be interpreted more easily by humans and integrated with symbol-oriented mechanisms is the subject of the first group of papers. The second group of papers uses specific applications as starting point, and describes approaches based on neural networks for the knowledge representation required to solve crucial tasks in the respective application.The companion first part of the special issue [1] contains papers dealing with representation and reasoning issues on the basis of neural networks.     

基于数据库的知识表达与推理研究

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。     

基于常识的亲属关系推理模型

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。     

模糊认知图研究进展

模糊认知图(Fuzzy Cognitive Map, FCM)作为知识表示、推理和软计算方法,通过在传统认知图模型中引入模糊测度来量化概念(concept)间因果关系的影响程度,近年来已成为国内外的研究热点。从研究进展的视角,归纳了FCM的基本框架和推理机制,总结了主流研究中FCM的基本类型,分析了FCM学习算法的主要特征,提出了今后专题研究方向的基本设想,以期对后续研究有所助益。     

A causal approach to nonmonotonic reasoning

We introduce logical formalisms of production and causal inference relations based on input/output logics of Makinson and Van der Torre [J. Philos. Logic 29 (2000) 383–408]. These inference relations will be assigned, however, both standard semantics (giving interpretation to their rules), and natural nonmonotonic semantics based on the principle of explanation closure. The resulting nonmonotonic formalisms will be shown to provide a logical representation of abductive reasoning, and a complete characterization of causal nonmonotonic reasoning from McCain and Turner [Proc. AAAI-97, Providence, RI, 1997, pp. 460–465]. The results of the study suggest production and causal inference as general nonmonotonic formalisms providing an alternative representation for a significant part of nonmonotonic reasoning.     

产品维修性设计专家系统构想与研究

文章对专家系统技术在产品维修性设计中的应用进行了研究,提出了该系统的总体结构方案和特点、知识表示和知识获取、推理机技术,并对计算机辅助维修性设计现状进行了简要分析。     

老年人看护系统中常识推理的研究

根据老年人看护的需求,将常识推理运用到智能家庭老年人看护推理系统中,并将其与领域本体相结合,对系统实现的主要技术,即基于本体的常识表示与存储、规则的表示以及常识的推理进行探讨和构想,并通过典型应用实例验证了常识推理在老年人看护系统中的可行性、有效性及其重要性。     

基于实例的智能工艺设计系统

针对传统智能工艺设计系统的缺陷与不足，结合基于实例推理（Case-Based Reasoning,CBR）和基于规则推理（Rule-Based Reasoning,RBR)的方法，设计了一个基于实例的智能工艺设计系统，给出了工艺实例一个完整清晰的形式化描述，阐述了新零件与实例进行比较和匹配的策略和算法，在检索出相符的实例后，调用RBR方法对实例进行修正，最终完成复杂的工艺设计任务。     

Baseballs and arguments from fairness

This paper applies two argumentation schemes, argument from fairness and argument from lack of knowledge (along with other schemes of lesser prominence) to model the reasoning given by Judge McCarthy supporting his decision to divide the proceeds of a homerun baseball in the case of Popov v. Hayashi. Several versions of both schemes are explained and discussed, and then applied to the argumentation given by Judge McCarthy as the basis of the reasoning used to arrive at his decision. The scheme for argument from fairness is shown to be based on a special principle in Perelman’s theory of justice.     

行动推理中循环因果关系的研究

近年来行动推理的研究成为人工智能领域的热门问题,而结果问题是目前行动推理研究的核心问题之一．该文针对许多行动推理系统不能处理循环因果关系的问题,提出了解决方法．基于适当修改后的McCain和Turner的因果理论,该文提出了一种能处理循环依赖的理论转化方法．转化后的因果理论消除了循环依赖,而且可以采用单调推理方法．基于因果闭包语义,证明了转化前后的因果理论具有相同的模型．当因果关系不存在循环依赖时,该文方法得到与McCain,Turner方法(1997)相同的结果．     

Knowledge-based programs

Summary.  Reasoning about activities in a distributed computer system at the level of the knowledge of individuals and groups allows us to abstract away from many concrete details of the system we are considering. In this paper, we make use of two notions introduced in our recent book to facilitate designing and reasoning about systems in terms of knowledge. The first notion is that of a knowledge-based program. A knowledge-based program is a syntactic object: a program with tests for knowledge. The second notion is that of a context, which captures the setting in which a program is to be executed. In a given context, a standard program (one without tests for knowledge) is represented by (i.e., corresponds in a precise sense to) a unique system. A knowledge-based program, on the other hand, may be represented by no system, one system, or many systems. In this paper, we provide a sufficient condition for a knowledge-based program to be represented in a unique way in a given context. This condition applies to many cases of interest, and covers many of the knowledge-based programs considered in the literature. We also completely characterize the complexity of determining whether a given knowledge-based program has a unique representation, or any representation at all, in a given finite-state context. Received: October 1995 / Accepted: February 1997     

Using Abstract Resources to Control Reasoning

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.     

25 Years of Knowledge Acquisition

This special issue celebrates 25 years of Knowledge Acquisition and centers around a breathtaking essay by Brian Gaines, the founder and former Editor-in-Chief of our journal, who discusses the processes of knowledge acquisition, knowledge representation and knowledge sharing across the millennia, and emphasizes the essential role that these processes have played throughout history, as engines of human evolution. In addition to Gaines' paper, this issue also comprises ten short contributions from a number of other prominent Knowledge Acquisition researchers. These provide a broad range of perspectives on the field and reflect the vivacity and diversity of the research in this area. While the Knowledge Acquisition area is by and large healthy and thriving, the shift in the past decade from intelligent problem solving to data acquisition and management can be seen as somewhat ‘reductionist’ with respect to the original ambitions of the community. This special issue provides a timely reminder of the ambitious goals of the field, its interdisciplinary ethos, and the sheer fun that the community has had in the past 25 years in pursuing the objective of building intelligent and symbiotic systems. I trust that our readers will be inspired and excited by this set of brilliant essays.     

产生式系统的关系数据库实现

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。     

Neural Networks and Structured Knowledge: Knowledge Representation and Reasoning

This collection of articles is the first of two parts of a special issue on Neural Networks and Structured Knowledge. The contributions to the first part shed some light on the issues of knowledge representation and reasoning with neural networks. Their scope ranges from formal models for mapping discrete structures like graphs or logical formulae onto different types of neural networks, to the construction of practical systems for various types of reasoning. In the second part to follow, the emphasis will be on the extraction of knowledge from neural networks, and on applications of neural networks and structured knowledge to practical tasks.