论辩系统的动态性及其研究进展

论辩系统是一种非单调形式体系,能够支持个体Agent的推理决策和多Agent之间的有效交互.由于个体Agent的知识、观察信息和资源的动态性以及多Agent交互过程的动态性,在各类论辩系统中,论证及其攻击关系的动态性是普遍存在的.作为一个新的研究领域,有关论辩系统动态性的概念、理论和方法远未成熟.在介绍论辩系统相关概念的基础上,阐明论辩系统动态性的两个主要研究方向(正向动态性和逆向动态性),并讨论需要解决的开放性问题.围绕这些问题,简要回顾现有的理论和方法,并分析其特点和不足.     

一种支持自治计算的基于可废止逻辑的柔性Agent

以自治计算的研究为背景,利用可废止逻辑理论的非单调知识表征和推理机制,提出一种能够动态接受规则变更、灵活处理实时发生的规则冲突,并进行高效的非单调推理的柔性Agent模型.这种Agent既是自主的,又是可控的,而且可以在开放、动态的环境中通过合同与其他Agent进行协同工作.     

辩论的逻辑模型研究综述

辩论的逻辑模型是哲学、逻辑学和人工智能等多个领域的研究课题,在非单调推理、法律推理、决策支持和多Agent交互等领域有广泛应用。文中首先简要阐述辩论及辩论模型的基本概念。然后从对辩论建模和用辩论建模两个方面对目前的研究进行总结,分析现有的有影响的辩论模型特点及其存在的问题。最后,指出今后的研究方向和发展趋势。     

CAUPS系统中的时空推理机制与算法

动态城市规划方案仿真系统中的一个重要问题就是抽象和提取仿真对象之间涉及到的空间和时间逻辑关系,并以此作为仿真过程的基本准则和规范动态推理演算。为了建立CAUPS系统中的时空推理机制,针对城市规划方案的动态仿真过程中需要应用到的基本准则和规范,在传统时空关系描述和推理规范的基础上定义出适用于面向城市规划动态仿真的时空关系。考虑到城市规划过程中存在的非刚体对象,提出面向城市辅助规划系统方案应用结果仿真的时间空间推理规范,该规范能够非常好的支持如植被、水域等非刚体对象,并给出一个适合多Agent系统采用的时间空间推理规范执行解决方案,包括时间推理规范算法和空间推理规范算法。面向城市规划的时间推理规范算法和空间推理规范算法已经被成功应用于CAUPS系统底层的多Agent交互关系调整中。     

关于行动的推理

关于行动的推理是研究行动及其所带来的变化的规律的一个人工智能分支。近年来,关于行动的推理技术一直伴随着非单调推理等一系列相关领域的开展而发展,出现了一系列新的理论与方法。本文介绍关于行动的推理的基本问题、理论与方法。同时也介绍与其它相关领域的联系。     

BDI Agent推理模型的研究

文章提出了基于环境感知的具有推理能力的BDI Agent模型。并通过引入环境感知函数、意见函数、过滤函数、行为函数对环境的变化和Agent自身推理过程进行研究。在此基础上,通过积木世界的具体实例加以说明。     

ESDDTL系统推理模式设计

本文通过对ESDDTL—腰腿痛疾病诊疗专家系统的开发与实践,研究、学习、设计了一种较有实效的不精确推理模式,介绍了医学诊疗专家系统ESDDTL推理模式的设计,并运用非单调推理理论进行了部分实践。     

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．     

基于多Agent混合推理的故障诊断方法

提出了基于正反向混合推理的故障诊断疗法,运用基于动态模糊综合评判的正向推理生成候选故障集,作为反向推理的基础。基于诊断任务的多层次混合分解策略,提出了融合不同知识表达方式和推理机制的多Agent并行诊断结构,分析了管理Agent、诊断Agent和决策Agent的基本职能,给出了并行推理过程中的冲突消解算法。     

故障诊断系统中基于多Agent的并行推理机制研究

基于诊断任务的多层次混合分解策略,提出了一种融合不同知识表达方式和推理机制的多Agent并行诊断结构,分析了管理Agent、诊断Agent和决策Agent的基本职能;并将证据理论应用于多诊断Agent的联合决策过程,指出了Dempster合成规则及Yager改进方法的不足,提出了基于证据支持度的冲突信息加权分配方法,并以一个算例证明了该方法的有效性。     

Well-founded semantics and stratification for ordered logic programs

This paper present an extension of traditional logic programming, called ordered logic (OL) programming, to support classical negation as well as constructs from the object-oriented paradigm. In particular, such an extension allows to cope with the notions of object, multiple inheritance and non-monotonic reasoning. The contribution of the work is mainly twofold. First, a rich wellfounded semantics for ordered logic programs is defined. Second, an efficient method for the well-founded model computation of a meaningful class of ordered logic programs, called stratified programs, is provided.     

An improved factor based approach to precedential constraint

In this article I argue for rule-based, non-monotonic theories of common law judicial reasoning and improve upon one such theory offered by Horty and Bench-Capon. The improvements reveal some of the interconnections between formal theories of judicial reasoning and traditional issues within jurisprudence regarding the notions of the ratio decidendi and obiter dicta. Though I do not purport to resolve the long-standing jurisprudential issues here, it is beneficial for theorists both of legal philosophy and formalizing legal reasoning to see where the two projects interact.     

耶鲁枪击问题之分析

本文以耶鲁枪击问题为例,分析了典型的非单调理论在解决框架问题上的局限性;在综述了已有的解决方案所存在的问题后,指出了非单调的推理与逻辑程序设计系统GKD—NMRS在描述和解决框架问题上的简洁性和直观性。     

机器推理的进展与展望

机器学习算法的发展仍受到泛化能力较弱、鲁棒性较差、缺乏可解释性等问题的限制.文中介绍机器推理,说明推理对于机器学习人的知识和逻辑、理解和解释世界的重要作用.首先分析人类大脑推理机制,从认知地图、神经元和奖赏回路,扩展到受脑启发的直觉推理、神经网络和强化学习.进而总结机器推理的方式及其相互关联的现状、进展及挑战,具体包括直觉推理、常识推理、因果推理和关系推理等.最后展望机器推理的应用前景与未来的研究方向.     

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.     

A new method for explanation-based learning

In this paper we discuss using the stratified ATMS to realize explanation-based learning. As the stratified ATMS can record and maintain the reasonings for beliefs efficiently and can deal with non-monotonic reasoning, so the ATMS-based EBL system can improve the efficiency of explanation-based learning, deal with multiple explanation problems in learning from imperfect theories by prioritized reasoning and multiple example verification and can give biases for induction in integrated learning.     

Generation and Selection of Abductive Explanations for Non-Omniscient Agents

Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking for explanations, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective. In the first part we explore syntactic definitions of both an abductive problem in terms of an agent’s information and an abductive solution in terms of the actions that modify the agent’s information. We look at diverse kinds of agents, including not only omniscient ones but also those whose information is not closed under logical consequence and those whose reasoning abilities are not complete. In the second part, we look at an existing logical framework whose semantic model allows us to interpret the previously stated formulas, and we define two actions that represent forms of abductive reasoning.     

环境智能中上下文推理方法研究综述

上下文推理是环境智能研究的核心问题之一,与环境智能系统的觉察、响应及适应能力紧密相关,近年来受到国内外研究者的广泛关注。文中分析介绍上下文推理的主要研究内容、研究方法和研究进展,并探讨目前存在的问题及未来的发展方向。