独立于设计者的行动推理

高水平的智能机器人要求能够独立地对环境进行感知并进行正确的行动推理.在情境演算行动理论中表示带有感知行动及知识的行动推理需要外部设计者为agent写出背景公理、感知结果及相应的知识变化,这是一种依赖于设计者的行动推理.情境演算行动理论被适当扩充,感知器的表示被添加到行动理论的形式语言中,并把agent新知识的产生建立在感知器的应用结果之上.扩充后的系统能够形式化地表示机器人对环境的感知并把感知结果转换为知识,还能进行独立于设计者的行动推理,同时让感知行动的“黑箱”过程清晰化.     

智能体行动推理中的信念修正

信念修正中的加强修正算法在保留低秩非矛盾信念时存在不足,流演算由于其表示公式的局限,无法较好的与信念修正结合来进行行动推理。针对以上不足,提出了依赖信念修正算法和Strategy-Axiom-Reasoning模型。该算法满足Ind假设并且较好的保留了条件信念和低秩非矛盾信念,SAR模型继承并改进了流演算的公理系统,用公式集表示信念集,较好的实现了与信念修正的结合。最后将依赖信念修正算法运用到SAR模型中,用实验证明了其可行性。     

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。     

基于进程演算和知识推理的安全协议形式化分析

安全协议的形式化分析是当前安全协议研究的热点,如何扩充现在已经成熟的理论和方法去研究更多的安全性质,使同一系统中各种安全性质在统一的框架下进行分析和验证是一个亟待解决的问题.进程演算是一强有力的并发系统建模工具,而结合知识推理可以弥补进程演算固有的缺乏数据结构支持的特点,以此提出了一个安全协议形式化分析的一般模型.基于此模型,形式化地定义了一些安全性质,给出了一个实例研究,并指出了进一步完善此模型的研究方向.     

命题逻辑在判断推理中的应用

以命题逻辑为基础,从等值演算、自然推理的方法出发,对判断推理问题进行分类并给出详细的分析和解答,将分析研究结果应用在教学实践中,提高命题逻辑的应用能力。     

直觉线性μ-演算中的合成推理

讨论了以基于前缀封闭集合的Heyting代数的直觉解释的线性μ-演算(IμTL)作为描述“假设-保证”的逻辑基础的问题,提出了一个基于IμTL的“假设-保证”规则.该规则比往常应用线性时序逻辑(LTL)作为规范语言的那些规则具有更好的表达能力,扩展了对形如“always ?”等安全性质的“假设-保证”的范围,具备更一般的“假设-保证”推理能力及对循环推理的支持.     

基于流演算的智能体行动推理模型设计与实现

并发约束程序设计在人工智能程序设计领域中占据越来越重要的位置,约束处理规则作为新一代的并发程序设计正倍受关注.对约束处理规则和流演算理论及其实现语言FLUX进行了研究,结合流演算和JCHR推理模型优点,设计了一种基于Java的流演算解释器JFLUX,同时提出了一个基于目标驱动的,在不完全可知的虚拟环境中通过感知到的有限信息进行自主行动推理能力的智能体模型,实现了办公室场景中智能体行动推理系统.     

基于状态演算的通用游戏系统的自动推理

为提高通用游戏系统的自动推理能力,提出了基于状态演算的通用游戏系统的自动推理方法,并将其作为推理模块构建了通用游戏系统模型.利用状态演算逻辑程序设计语言(state calculus executor,STEX)实现通用游戏的状态更新与动作推理,给出了基于状态演算的通用游戏系统模型的功能模块设计,最后利用prolog语言实现了各功能模块,调试系统并进行实例验证.实验结果表明,采用了基于状态演算的自动推理方法,得到较好的游戏成绩,提高了游戏系统的自动推理能力.     

基于程序窗口推理的精化演算

由于数据精化需要针对更大的程序块, 所以,它比一般的算法精化更加复杂.在精化演算中过程如何有效地进行数据精化是形式化 方法研究中的一个重要内容.该文介绍了相关的基本概念.在精化演算的基础上,构造了一种 数据精化算子,并提出一种基于数据精化演算和程序窗口推理的数据精化的方法.     

事件本体中的动作推理研究

事件是随时间变化而变化的具体事实,事件是由动作、时间及其它要素组成,动作是事件定义中的主要构成部分.在面向事件的知识库系统中,关于动作的推理研究一直是重要的研究课题之一.现有的动作推理形式化系统旨在描述和推理现实世界中状态的变化,忽略了时间要素对推理过程的影响.针对这种不足,本文在描述逻辑的基础上扩充了一个Action-TBox和一个Action-ABox,并将事件本体中的动作要素和时间要素相结合,形式化定义了动作的一个三元组表示方式以及多种时间构造算子,用以刻画组合动作的发生过程,在此基础上研究了事件本体中关于动作的几种推理服务.     

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.     

Iterated belief change in the situation calculus

John McCarthy's situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change.     

Event calculus and temporal action logics compared

We compare the event calculus and temporal action logics (TAL), two formalisms for reasoning about action and change. We prove that, if the formalisms are restricted to integer time, inertial fluents, and relational fluents, and if TAL action type specifications are restricted to definite reassignment of a single fluent, then the formalisms are not equivalent. We argue that equivalence cannot be restored by using more general TAL action type specifications. We prove however that, if the formalisms are further restricted to single-step actions, then they are logically equivalent.     

From event calculus to the scheduling problem. Semantics of action and temporal reasoning in aircraft maintenance

In this work we are interested in the logical and semantical aspects of reasoning about actions in a scheduling process. We present an adaptation of the event calculus of Kowalski and Sergot to the problem of determining the temporal structure of the operations that must be performed during the realization of some complex objectives. Our application domain is aircraft maintenance. We try to reason about the actions which are performed during an overhaul in order to help to schedule them. The original model reasons about changes, i.e. events which initiate or terminate propositions. The first step of this work was to improve the initial model by adding a temporal relation between events and propositions because in our field we also have to reason about events which only inform us about some propositions without affecting them. The second step of this work is to build a set of specific rules which temporally interpret the semantics of the usual specifications of the actions to be considered. This interpretation aims to associate each action with two events and some temporal relations which are usable by the general model. Temporal reasoning uses pertinent knowledge about the specific universe (here, the aircraft that we consider and the actions which may be performed on it). We outline a generative methodology to formalize this relevant knowledge efficiently. This cognitive approach brings more informational economy in temporal reasoning because only the relevant information is considered The temporal reasoning model and the methodology have been exemplified and tested on a complex part of an aircraft. In the future, adapted tools based on this approach will be developed, in order to solve several problems of aircraft maintenance scheduling.     

基于流演算的智能虚拟人模型研究与实现*

在研究流演算理论及其实现语言FLUX的基础上,将流演算与虚拟现实技术中的虚拟人相结合,提出了一个基于目标驱动的、有自主行动能力的虚拟人模型。设计了动作检测模块,同时使用了动作队列,根据动作检测的结果来决定是否执行下一个动作,使虚拟人可以针对动态变化的虚拟环境进行有效的行动规划。利用此模型可以快速构建出一个在不完全可知的虚拟环境中通过感知到的有限信息进行实时的、自主行动推理的智能虚拟人。最后,实现了办公室场景中智能虚拟人行动推理系统。     

A New Method for Reasoning about Action

Reasoning about action is an important aspect of common sense reasoning and planning.It gives rise to three classical problems:the frame problem,the qualification problem and the ramification problem.Existing approaches cannot deal with these problems efficiently.This paper presents a new method which uses the stratified ATMS for reasoning about action to overcome the limitations of these approaches.     

A preferential semantics for causal reasoning about action

One of the principal concerns in the research area of Reasoning about Action is determining the ramifications of actions in changing environments. A particular tendency emerging in recent literature endorses the explicit incorporation of causal knowledge in logic-based action theories. It is argued that causal extensions not only enhance the expressive power of theories of action, but may also provide more concise and intuitive representations. This paper investigates semantics for causal reasoning about action and change. It does so by exploring the role of several fundamental underlying principles, such as the Principle of Minimal Change and the Principle of Causal Change. This work culminates in a general unifying semantics for a class of action theories represented by a number of recent and influential approaches – in particular, the causal relationship approach of Thielscher and the causal systems with fixed-points suggested by McCain and Turner. The unifying augmented preferential semantics, emerging as a result of this study, captures both Principles of Change and shows their clear and distinct roles.     

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.