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。     

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

Automated inspection process planning: Algorithmic inspection feature recognition, and inspection case representation for CBR

General metrological inspection planning is among the least explored computer-aided process planning (CAPP) domains. This paper explores certain basic issues involved in inspection planning using case-based reasoning in an environment of a Generic CAPP Support System. Firstly, algorithmic methods for characterizing and extracting inspection features are proposed and discussed. A sequential knowledge based filtering method is developed to reduce the number of inspection features typically encountered in metrological inspection planning. Finally, a formalized approach for case representation of relevant inspection domain knowledge using a newly developed parametric-list technological feature graph (PLTFG) is presented.     

一种基于产生式规则的不确定推理模板模型的研究

该文针对现有不确定推理模型,结合专家知识不确定性在产生式规则系统中的体现,归纳出一种更接近人类专家处理不确定性和方便人们理解与构造实例模型的不确定推理模板模型。该模板模型可作为现有模型的分析模板和新的基于产生式规则的不确定推理模型研究与构造的基本框架模板。该文详细阐述了该模板模型的组成和工作原理,并用它对现有不确定推理模型进行了实例分析;最后,指出该模板模型各组成子模型的研究方向。     

COMPUTATION OF BEST BOUNDS OF PROBABILITIES FROM UNCERTAIN DATA

An uncertainty reasoning method is presented in this article. The method can be used to compute from a given set of conditional probabilities the best lower bounds and the best upper bounds of those conditional probabilities that are not explicitly provided. The computation of the best upper(lower) bound of such a conditional probability relies on solution of a linear programming problem. Some reduction techniques are proposed in this article to improve the efficiency of our uncertainty reasoning method. As illustrated in Section 4.3, for many uncertainty reasoning problems in medical diagnosis, by using our reduction techniques, the best range of a conditional probability, which is specified by a lower bound and an upper bound, can be computed in polynomial time in terms of the number of basic events involved in the reasoning.     

一种集成式不确定推理方法研究

摘要集成学习是采用某种规则把一系列学习器的结果进行整合以获得比单个学习器更好的学习效果的一种机器学习方法。研究表明集成学习是可行的,能取得比传统学习方法更好的性能。不确定推理是人工智能的重要研究方向之一,目前已经开发出了多种不确定推理方法,这些方法在实际应用中各有优缺点。借鉴集成学习,提出一种集成式不确定推理方法,其基本思想是按照一定的策略集成多种不确定推理方法,以提高推理的准确性。理论分析和实验结果验证了方法的合理性和可行性。     

Integrating the IS functions after mergers and acquisitions: Analyzing business-IT alignment

This exploratory positivist case study uses multiple case design to examine the impacts of strategic alignment during the mergers and acquisitions (M&A) process and the implications for strategic formulation of successful M&A outcomes. According to alignment theory, for a firm to achieve M&A integration success, the business-IS strategies should be aligned during the M&A execution. However, our results indicate that successful, non-aligned M&A integrations can also occur, strongly supporting the emergent perspective of strategy formation in M&A integration as a valuable addition to the a priori formal planning view of strategy formation.     

Default reasoning by deductive planning

This paper deals with the automation of reasoning from incomplete information by means of default logics. We provide proof procedures for default logics' major reasoning modes, namely, credulous and skeptical reasoning. We start by reformulating the task of credulous reasoning in default logics as deductive planning problems. This interpretation supplies us with several interesting and valuable insights into the proof theory of default logics. Foremost, it allows us to take advantage of the large number of available methods, algorithms, and implementations for solving deductive planning problems. As an example, we demonstrate how credulous reasoning in certain variants of default logic is implementable by means of a planning method based on equational logic programming. In addition, our interpretation allows us to transfer theoretical results, such as complexity results, from the field of planning to that of default logics. In this way, we have isolated two yet unknown classes of default theories for which deciding credulous entailment is polynomial.Our approach to skeptical reasoning relies on an arbitrary method for credulous reasoning. It does not strictly require rather the inspection of all extensions, nor does it strictly require the computation of entire extensions to decide whether a formula is skeptically entailed. Notably, our approach abstracts from an underlying credulous reasoner. In this way, it can be used to extend existing formalisms for credulous reasoning to skeptical reasoning.This author was a visiting professor at the University of Darmstadt while parts of this work were being carried out. This author also acknowledges support from the Commission of the European Communities under grant no. ERB4001GT922433.     

视觉导引可移动机器人中的空间不确定性的表示和推理

本文通过对视觉导引可移动机器人建模、导航和规划中涉及的空间不确定性问题的研究, 提出了一套系统的空间不确定性的表示和推理方法.     

基于确定因子的不确定性推理的仿真实现

针对传统的基于确定因子的不确定性推理模型在实际应用中出现的几个问题,提出了一种新型的以数理统计理论为基础、通过采用计算机模拟的方法来实现不确定性推理模型中相关参数的算法模型,并对基于确定因子的不确定性推理模型的相关知识作了详细叙述。根据所给出的算法模型,以地震预报专家系统中某条知识表示的规则强度CF的值的得出为例,完成了对CF的模拟,详细阐述了方法的实现过程并对模拟出的值的可信程度进行了检验,结果证明模拟技术为人工智能研究领域的不确定性推理的应用提供一种新的方法。     

Knowledge-based treatment planning for adolescent early intervention of mental healthcare: a hybrid case-based reasoning approach

Abstract: Treatment planning is a crucial and complex task in the social services industry. There is an increasing need for knowledge-based systems for supporting caseworkers in the decision-making of treatment planning. This paper presents a hybrid case-based reasoning approach for building a knowledge-based treatment planning system for adolescent early intervention of mental healthcare. The hybrid case-based reasoning approach combines aspects of case-based reasoning, rule-based reasoning and fuzzy theory. The knowledge base of case-based reasoning is a case base of client records consisting of documented experience while that for rule-based reasoning is a set of IF–THEN rules based on the experience of social service professionals. Fuzzy theory is adopted to deal with the uncertain nature of treatment planning. A prototype system has been implemented in a social services company and its performance is evaluated by a group of caseworkers. The results indicate that hybrid case-based reasoning has an enhanced performance and the knowledge-based treatment planning system enables caseworkers to construct more efficient treatment planning in less cost and less time.     

Extending the Maximum Entropy Approach to Variable Strength Defaults

A generalisation of the maximum entropy (ME) approach to default reasoning [7,8] to cater for variable strength defaults is presented. The assumptions on which the original work was based are reviewed and revised. A new algorithm is presented that is shown to compute the ME-ranking under these more general conditions. The limitations of the revised approach are discussed and a test for the uniqueness of the ME-solution is given. The ME-solutions to several illustrative examples of default reasoning are given, and the approach is shown to handle them appropriately. The conclusion is that the ME-approach can be regarded as providing a benchmark theory of default reasoning against which default intuitions and other default systems may be assessed.     

An ontological intelligent agent platform to establish an ecological virtual enterprise

A virtual enterprise (VE) is a dynamic alliance of companies collaborating for the accomplishment of a specific business goal. To establish a VE, it is very important for the VE initiator to select appropriate partners. General criteria such as price, lead time, quality, etc. are the major concerns for most VE initiators. However, in today’s environmentally conscious society, environmental issues such as enterprise green image, product eco-design, etc. are increasingly receiving attention. Thus, it is worth to research on how to select the appropriate collaborative partners to establish an ecological VE.The objective of this paper is to establish a multi-agent system platform for individual companies to form an ecological VE based on ontology theory and intelligent agents. The ontological approaches include shared ontology construction, ontology matching, ontology integration, ontology storage and ontology reasoning. In the generalized case that the VE initiator is a manufacturer and the collaborating partner are suppliers, the multi-agent system comprises three types of intelligent agents, namely, knowledge manager agent (KMrA), manufacturer agent (MA) and supplier agent (SA). MA and SA represent the capabilities and interests of the VE initiator and the VE partners, respectively. KMrA is in charge of functioning sub-tasks of the ontological approach. To select partners for the ecological VE, the VE initiator will also consider the environmental criteria, in addition to the general supplier selection criteria such as price, quantity, quality and lead time. The environmental criteria may include factors such as environmental management, green image, green product and pollution control. The complete set of selection criteria, including the environmental criteria, are categorised into quantitative or qualitative criteria. The formation of ecological VE is then divided into two stages, that is, candidate supplier selection based on qualitative criteria, and ultimate supplier selection based on quantitative criteria. A simplified example is introduced to illustrate and justify the proposed ontological approaches and intelligent agent platform.     

基于云模型的入侵检测研究

由于网络行为的不确定性,使现有入侵检测系统几乎都存在高误报率和高漏报率的缺点。云模型是将模糊性和随机性有机结合进行不确定性推理的有效工具。本文利用云模型来处理网络实体行为的不确定性,提出了一种新的云入侵检测方法。该方法通过基于云知识库的云推理引擎进行不确定性推理,以对网络实体行为进行智能判断。模拟结果表明该方法能有效提高入侵检测效率。     

Reasoning about Locations in Theory and Practice

Locational reasoning plays an important role in many applications of AI problem-solving systems, yet has remained a relatively unexplored area of research. This paper addresses both theoretical and practical issues relevant to reasoning about locations. We define several theories of location designed for use in various settings, along with a sound and complete belief revision calculus for each that maintains a STRIPS-style database of locational facts. Techniques for the efficient operationalization of the belief revision rules in planning frameworks are presented. These techniques were developed during application of the location theories to several large-scale planning tasks within the Sipe planning framework.     

Interactive Case-Based Planning for Forest Fire Management

This paper describes an AI system for planning the first attack on a forest fire. This planning system is based on two major techniques, case-based reasoning, and constraint reasoning, and is part of a decision support system called CHARADE. CHARADE is aimed at supporting the user in the whole process of forest fire management. The novelty of the proposed approach is mainly due to the use of a local similarity metric for case-based reasoning and the integration with a constraint solver in charge of temporal reasoning.     

基于Ontology的平面几何知识库设计

文中给出了Ontology建模方法在平面几何知识库设计中的一个应用实例，先从总体上介绍了Ontology建模方法的思想，框架结构及优点，再详细描述了平面几何知识库的设计、组织结构及其实现，我们提出概念分层，等价类存贮、启发式双向推理、推理经验生成，交互解题，数字测试等多种平面几何知识库的相关技术，能生成与“吴法”等代数方法不同的、传统的、较优的可读证明，并提高了系统的重用性、可靠性、可维护性和知识扩展性。     

青铜器鉴定专家系统中的不确定性推理

讨论了青铜器鉴定专家系统的不确定性推理方法,包括知识的不精确表示,推理规则的不精确表示,组合证据的不精确描述以及推理规则的更新。并将该推理方法应用干文物鉴定领域,成功建立了青铜器鉴定专家系统。