基于云模型以约束为中心的复杂系统定性建模

针对以约束为中心的复杂系统仿真中缺乏有效的不确定性信息描述方法,导致仿真中不确定性信息不能充分利用的问题,借鉴和采纳云模型的相关理论和方法,研究了以约束为中心基于云模型的复杂系统定性建模方法。首先提出了仿真中基于云模型的不确定信息表示方法、基于云模型和群体专家决策的量空间构建方法;然后给出了基于云模型的系统定性约束方程的构建方法;最后将提出的建模方法应用于一个敏捷供应链的建模实例。结果表明提出的建模方法具有客观表达不确定信息、将定性与定量信息在仿真中相互融合的优点。     

A review of: “LOGIC AND PHILOSOPHY IN THE LVOV-WARSAW SCHOOL”, by Jan Woleński,Kluwer, Dordrecht (The Netherlands) and Boston, 1989. xiv + 364 pages.

Qualitative methodology plays an important role within computer simulation; modeling and analysis of complex systems require qualitative methods since humans think naturally in qualitative and linguistic terms. The critical interface for simulationists exploring qualitative simulation should rely on an unambiguous mathematical formalism or method with foundations in systems theory. Currently, many ad hoc formalisms exist for encoding uncertain or qualitative simulation knowledge; however, we have found that fuzzy set theory provides for a formalism where linguistic variables can be encoded as state, parameter, input and output information in the model. Fuzzy numbers, in particular, are useful when population statistics are unavailable—usually due to cost factors. We have constructed fuzzy simulation programs based on our C-based SimPack library and we use fuzzy simulation to hypothesize qualitative system models reflecting real system behavior, and to specify qualitative versions of systems.     

Qualitative design support for engineering and architecture

Conventional design support software tools cannot effectively manage the complex, heterogeneous information used in engineering and architecture (EA) tasks. Crucially, despite uncertainty being an inherent quality of EA information particularly in the early stages of a design project, current tools solely rely on numerical approaches which do not support such incomplete and vague information. In this paper, we establish a complete framework for developing qualitative support tools that directly address these shortcomings. Our framework is application oriented and addresses the broader issues surrounding the actual use of qualitative methods. It provides design principles and strategies that allow a software engineer to develop custom qualitative software tools according to their specific EA task specifications. Our framework also provides the engineer with practical theory and guidelines for implementing their custom qualitative model and validating their system using context specific test data. We demonstrate the validity of our framework by presenting a case study in architectural lighting in which a prototype qualitative reasoning engine successfully automates qualitative logic about the subjective impressions of a lighting installation.     

可信度增强的模糊定性仿真

为了建立一种有效的形式化方法,期望利用对象的不完全信息得到系统行为最为重要 的结论,结合定性推理、模糊集合的概率论三种理论,发挥模糊集合在常识知识描述和概率论在 解决随机性上的优点,本文提出了一种可信度增强的模糊定性仿真方法.     

GM（1，N）和QSIM结合的复杂系统的定性仿真建模方法

针对复杂系统仿真中系统信息缺乏、QSIM建模方法可使用微分方程的特点,在复杂系统仿真方法中,提出了一种GM（1,N）和QSIM相结合的定性建模方法.首先给出了相关研究的现状,然后提出了GM（1,N）和QSIM相结合的仿真建模方法的基本原理和主要过程.最后通过一个系统仿真建模实例验证了该方法的可行性.结果表明,该方法具有充分利用系统较少信息,能将定量和定性信息有效地融合与复杂系统的仿真建模之中的特点.     

Binary naive possibilistic classifiers: Handling uncertain inputs

Possibilistic networks are graphical models particularly suitable for representing and reasoning with uncertain and incomplete information. According to the underlying interpretation of possibilistic scales, possibilistic networks are either quantitative (using product‐based conditioning) or qualitative (using min‐based conditioning). Among the multiple tasks, possibilitic models can be used for, classification is a very important one. In this paper, we address the problem of handling uncertain inputs in binary possibilistic‐based classification. More precisely, we propose an efficient algorithm for revising possibility distributions encoded by a naive possibilistic network. This algorithm is suitable for binary classification with uncertain inputs since it allows classification in polynomial time using several efficient transformations of initial naive possibilistic networks. © 2009 Wiley Periodicals, Inc.     

动态系统的定性推理：定性模型的建立与定性仿真方法

本文首先介绍了动态系统的定性推理的产生背景，发展概况以及定性推理的实际应用前景，然后以Ｋｕｉｐｅｒｓ的定性仿真理论为中心介绍了定性推理的基本原理及方法，其中重点介绍了动态定性仿真算法ＱＳＩＭ和定性推理的最新发展，并对存在的问题进行了探讨。     

PROLEGOMENA TO ANY FUTURE QUALITATIVE PHYSICS

We evaluate the success of the qualitative physics enterprise in automating expert reasoning about physical systems. The field has agreed, in essentials, upon a modeling language for dynamical systems, a representation for behavior, and an analysis method. The modeling language consists of generalized ordinary differential equations containing unspecified constants and monotonic functions; the behavioral representation decomposes the state space described by the equations into discrete cells; and the analysis method traces the transitory response using sign arithmetic and calculus. The field has developed several reasoners based on these choices over some 15 years. We demonstrate that these reasoners exhibit severe limitations in comparison with experts and can analyze only a handful of simple systems. We trace the limitations to inappropriate assumptions about expert needs and methods. Experts ordinarily seek to determine asymptotic behavior rather than transient response, and use extensive mathematical knowledge and numerical analysis to derive this information. Standard mathematics provides complete qualitative understanding of many systems, including those addressed so far in qualitative physics. Preliminary evidence suggests that expert knowledge and reasoning methods can be automated directly, without restriction to the accepted language, representation, and algorithm. We conclude that expert knowledge and methods provide the most promising basis for automating qualitative reasoning about physical systems.     

Performance assessment of a system for reasoning under uncertainty

From the early developments of machines for reasoning and decision making in higher-level information fusion, there was a need for a systematic and reliable evaluation of their performance. Performance evaluation is important for comparison and assessment of alternative solutions to real-world problems. In this paper we focus on one aspect of performance assessment for reasoning under uncertainty: the accuracy of the resulting belief (prediction or estimate). We propose a framework for assessment based on the assumption that the system under investigation is uncertain only due to stochastic variability (randomness), which is partially known. In this context we formulate a distance measure between the “ground truth” and the output of an automated system for reasoning in the framework of one of the non-additive uncertainty formalisms (such as imprecise probability theory, belief function theory or possibility theory). The proposed assessment framework is demonstrated with a simple numerical example.     

复杂系统的定性仿真

回顾复杂系统定性仿真的发展历程;阐述复杂系统定性仿真的基本原理,将定性仿真方法归纳为非因果关系推理、因果关系推理和元胞自动机三类;评述三类方法的研究和应用进展;探讨管理系统复杂性的特征,介绍本文作者及其合作者的管理系统定性仿真研究工作;给出复杂系统定性仿真下一步需要解决的关键问题。     

Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.     

Human centricity and information granularity in the agenda of theories and applications of soft computing

Soft computing is an interdisciplinary area that focuses on the design of intelligent systems to process uncertain, imprecise and incomplete information. It mainly builds on fuzzy sets theory, fuzzy logic, neural computing, optimization, evolutionary algorithms, and approximate reasoning et al. Information granularity is in general regarded as a crucial design asset, which helps establish a better rapport of the resulting granular model with the system under modeling. Human centricity is an inherent property of people's view on a system, a process, a machine or a model. Information granularity can be used to reflect people's level of uncertainty and this makes its pivotal role in soft computing. Indeed, the concept of information granularity facilitates the development of theory and application of soft computing immensely. A number of papers pertaining to some recent advances in theoretical development and practical application of information granularity in soft computing are highlighted in this special issue. The main objective of this study is to collect as many as possible researches on human centricity and information granularity in the agenda of theories and applications of soft computing, review the main idea of these literatures, compare the advantages and disadvantages of their methods and try to find the relationships and relevance of these theories and applications.     

定性Dempster-Shafer理论

采用一个全序的符号值集合来代替数值信任度集合[0,1],提出定性Dempster-Shafer理论来处理既有不确定性又有不精确性的推理问题．首先,定义了适合对不确定性进行定性表达和推理的定性mass函数、定性信任函数等概念,并且研究了这些概念之间的基本关系;其次,详细讨论了定性证据合成问题,提出了基于平均策略的证据合成规则．这种定性Dempster-Shafer理论与其他相关理论相比,既通过在定性领域重新定义Dempster-Shafer理论的基本概念,继承了Dempster-Shafer理论在不确定推理方面的主要特点,同时又具有适合对不精确性操作的既有严格定义又符合直观特性的定性算子,因此更适合基于Dempster-Shafer理论框架不精确表示和处理不确定性．     

Composing Cardinal Direction Relations Based on Interval Algebra

Direction relations between extended spatial objects are important commonsense knowledge. Skiadopoulos proposed a formal model for representing direction relations between compound regions (the finite union of simple regions), known as SK-model. It perhaps is currently one of most cognitive plausible models for qualitative direction information, and has attracted interests from artificial intelligence and geographic information system. Originating from Allen first using composition table to process time interval constraints; composing has become the key technique in qualitative spatial reasoning to check the consistency. Due to the massive number of basic directions in SK-model, its composition becomes extraordinary complex. This paper proposed a novel algorithm for the composition. Basing the concepts of smallest rectangular directions and its original directions, it transforms the composition of basic cardinal direction relations into the composition of interval relations corresponding to Allen''s interval algebra. Comparing with existing methods, this algorithm has quite good dimensional extendibility, that is, it can be easily transferred to the tridimensional space with a few modifications.     

Jeffrey’s rule of conditioning in a possibilistic framework

Conditioning, belief update and revision are important tasks for designing intelligent systems. Possibility theory is among the powerful uncertainty theories particularly suitable for representing and reasoning with uncertain and incomplete information. This paper addresses an important issue related to the possibilistic counterparts of Jeffrey’s rule of conditioning. More precisely, it addresses the existence and uniqueness of the solutions computed using the possibilistic counterparts of the so-called kinematics properties underlying Jeffrey’s rule of conditioning. We first point out that like the probabilistic framework, in the quantitative possibilistic setting, there exists a unique solution for revising a possibility distribution given the uncertainty bearing on a set of exhaustive and mutually exclusive events. However, in the qualitative possibilistic framework, the situation is different. In particular, the application of Jeffrey’s rule of conditioning does not guarantee the existence of a solution. We provide precise conditions where the uniqueness of the revised possibility distribution exists.     

Qualitative modelling and simulation by piecewise linear analysis

Since applications of expert systems were typical in domains with no well defined models, qualitative methods for modelling and reasoning were soon developed. Most current qualitative reasoning programs derive the qualitative behaviour of a system by simulating a hand-crafted qualitative version of the differential equation that caracterizes the model of a system. This paper describes a method to construct a family of piecewise linear dynamical systems from the qualitative information contained in a model. We apply the dynamical system theory to deduce results about the behaviour of the family of dynamical systems constructed as a consequence of the qualitative model.     

基于证据推理的多指标评价系统设计与实现

针对具有不确定性的混合多指标评价问题,介绍了定性指标和定量指标的数据提取方法,结合证据推理法的基本理论,采用混合编程技术,开发了证据推理评价系统。系统设计了指标体系管理、数据管理、评价分析三大模块,实现了定性指标与定量指标相结合的多指标综合评价。最后,以船舶保障性为示例验证了系统具有稳定性和实用性,该系统能够多角度分析评价结果,为决策者提供辅助决策。     

定性代数的形式框架FAQA

本文首先讨论了定性推理系统和一量推理系统的描述，进而给出了定性代数的一种形式框架ＦＡＱＡ，提出了定性、定量是不同层次观察结果的观点，并对定性（混合）结构进行了分析描述。ＦＡＱＡ框架是Ｗｉｌｌｉａｍｓ等人工作理论上的完善及统一基础。     

Information system project selection using fuzzy logic

The allocation of scarce business resources is becoming a major problem for management nowadays. Limited by lack of resources, management needs to make project selection decisions under the constraints of available information, and often makes decisions based on incomplete information. Traditionally, quantitative techniques dominate decision-making in selecting management information systems (MIS) projects. However, encapsulating or simply discarding the qualitative concerns makes a project economically sound but not operationally sound. This is often the reason that causes a project to fail. This paper establishes a model by incorporating fuzzy logic as a decision tool, which smoothly aids decision makers dealing with uncertain or incomplete information without losing existing quantitative information