Context reasoning using extended evidence theory in pervasive computing environments

Most existing context reasoning approaches implicitly assume that contexts are precise and complete. This assumption cannot be held in pervasive computing environments, where contexts are often imprecise and incomplete due to unreliable connectivity, user mobility and resource constraints. To this end, we propose an approach called CRET: Context Reasoning using extended Evidence Theory. CRET applies the evidence theory to context reasoning in pervasive computing environments. Because evidence theory is limited by two fundamental problems–computation-intensiveness and Zadeh paradox, CRET presents evidence selection and conflict resolution strategies. Empirical study shows that CRET is desirable for pervasive applications.     

Evolution of fuzzy logic: from intelligent systems and computation to human mind

Inspired by human’s remarkable capability to perform a wide variety of physical and mental tasks without any measurements and computations and dissatisfied with classical logic as a tool for modeling human reasoning in an imprecise environment, Lotfi A. Zadeh developed the theory and foundation of fuzzy logic with his 1965 paper “Fuzzy sets” (Zadeh in Inf Control 8:378–53, 1965) and extended his work with his 2005 paper “Toward a generalized theory of uncertainty (GTU)—an outline” (Zadeh in Inf Control, 2005). Fuzzy logic has at least two main sources over the past century. The first of these sources was initiated by Peirce in the form what he called a logic of vagueness in 1900s, and the second source is Lotfi’s A. Zadeh work, fuzzy sets and fuzzy Logic in the 1960s and 1970s.     

On possibility qualification in natural languages

The theory of possibility, recently introduced by L. A. Zadeh, is explored in the particular subject of possibility-qualified propositions as a basis for approximate reasoning. The concept of ε-possibility related to fuzzy sets has a formulation that verifies some criteria of minimal uncertainty. It is shown how to derive possibility-qualified propositions from the classical translation rules in fuzzy logic.     

Fuzzy modus ponens: A new model suitable for applications in knowledge-based systems

Models for fuzzy deductive reasoning are becoming important in the field of knowledge-based systems. Zadeh suggested a compositional rule of inference, also called Generalized Modus Ponens. In order to study the fine structure of this model, we test seven rules of implication, usually found in the fuzzy literature, and two kinds of compositional rule with several properties which seems to be required by commonsense reasoning. But, as no rule is shown to satisfy these properties, we propose a new model for fuzzy Modus Ponens. the main idea of this model is to find a fuzzy extension of the meta-level rule of inference, in comparison with the object-level extension of the disjunctive syllogism such as the Zadeh's model. It is shown that our model satisfies all the intuitively required properties, and moreover is computationally very easy to apply.     

What a machine should know about philosophical problems?

This paper maps out some of the few remaining challenges to artificial intelligence from the point of view of philosophy and fuzzy logic. Certain key ideas of Lotfi Zadeh are used as a point of reference. Human reasoning is a complex procedure which is able to handle such problems as the counterfactual truth and the supervenience relation, which are difficult to explain in terms of the classical logical theory. To achieve this, one needs to understand them in a more natural manner than the standard positivistic logic is able to do. Next some examples taken from ethical theory are discussed, such as the fact/value-distinction and supererogation. These provide typically hard cases to the positivist methodology, as its representatives have always admitted. The results are applied to the philosophical problems of robotics, especially to the notion of a Cyborg on the information web. A Cyborg on the Web should be able to combine human-like reasoning with unlimited information processing.     

一种基于改进证据理论的推理决策方法

针对证据理论的Zadeh悖论问题,提出了一种基于冲突一致度与交并集动态调整的推理决策方法。首先,该方法基于对冲突度与一致度的综合考虑,引入冲突一致度的概念,并基于冲突一致度对多源证据进行折扣操作。其次,基于交并集权重的动态调整对多源证据进行融合。然后,基于最大信任做出推理决策。最后,使用MATLAB构建仿真算例来对提出的方法与典型的证据合成方法进行比较验证。实验表明,该方法切实有效,能够避免悖论问题的产生,推理结果的区分能力优于典型方法。     

METHODS OF REVISION PRINCIPLE FOR APPROXIMATE REASONING

Abstract

Compositional inference and compatibility-modification inference are two main approaches for approximate reasoning (Baldwin, 1979a; 1979b; Dubois, 1985; Mizumoto, 1981; Mizumoto. 1987; Tsukamoto, 1979; Yager, 1980; Zadeh, 1975a; 1975b; 1979). The former realizes inference by obtaining an implication relation between antecedent and consequent of a rule and then composing the input with the relation (Zadeh, 1975a). The latter realizes inference by determining the measure of satisfaction between input and antecedent of a rule and then using the measure to modify the rule's consequen(Dubois, 1985). The revision principle was proposed in a different way: it is under such a belief that the modification (revision) of consequent should be caused only by the difference (deviation) between input (given fact) and antecedent. In other words, when a method of revision principle is used to approximate reasoning the consequent will always be obtained as output if input is the same as the antecedent. The revising processing is based on some kind of relation between antecedent and consequent, which can be linear relation or semantic relation. We introduce five revising methods and then evaluate them by relation keeping property.     

Qualitative reasoning based on fuzzy relative orders of magnitude

This paper proposes a fuzzy set-based approach for handling relative orders of magnitude stated in terms of closeness and negligibility relations. At the semantic level, these relations are represented by means of fuzzy relations controlled by tolerance parameters. A set of sound inference rules, involving the tolerance parameters, is provided, in full accordance with the combination/projection principle underlying the approximate reasoning method of Zadeh. These rules ensure a local propagation of fuzzy closeness and negligibility relations. A numerical semantics is then attached to the symbolic computation process. Required properties of the tolerance parameter are investigated, in order to preserve the validity of the produced conclusions. The effect of the chaining of rules in the inference process can be controlled through the gradual deterioration of closeness and negligibility relations involved in the produced conclusions. Finally, qualitative reasoning based on fuzzy closeness and negligibility relations is used for simplifying equations and solving them in an approximate way, as often done by engineers who reason about a mathematical model. The problem of handling qualitative probabilities in reasoning under uncertainty is also investigated in this perspective.     

A Fuzzy Proof Theory

Based on the first order peicate logic,in this paper,we present a new approach to generalizing the syntax of ordinary Horn clause rules to establish a fuzzy proof theory,First of all,each Horn clause rule is associated with a numerical implication strength f.Therefore we obtain f-Horn clause rules.Secondly,Herbrand interpretations can be generalized to fuzzy subsets of the Herbrand base in the sense of Zadch.As a result the proof theory for Horn clause rules can be developed in much the same way for f-Horm clause rules.     

On modeling of linguistic information using random sets

This paper discusses the formal connection between possibility distributions (Zadeh [21]) and the theory of random sets via Choquet's theorem. Based upon these relationships, it is suggested that plausible inferences and modeling of common sense can be derived from the statistics of random sets. The analysis of subjectivity in meaning representation of natural languages can be carried out by taking account of covariates of individuals as in the statistical analysis of survival data.     

LINGUISTIC HEDGES: THEIR RELATION TO CONTEXT AND THEIR EXPERIMENTAL REALIZATION

Abstract

We first discuss the fuzzy subset representation of the class of monotonic type linguistic values, i.e., small and large. We next show that for each of these the context, i.e., large apartment, determines the window or range in which the significant change in membership degree occurs. We discuss Zadehs approach to modifying a linguistic value by a hedge such as “very.” We next show that one interpretation of the effect of this hedge is to act as a context changer. We finally reconcile the experimental realizations of the effect of linguistic hedges with the approach suggested by Zadeh.     

Systemic approach to fuzzy logic formalization for approximate reasoning

L.A. Zadeh, E.H. Mamdani, M. Mizumoto, et al., R.A. Aliev and A. Tserkovny have proposed methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). A formulation of fuzzy antecedent/consequent chains is one of the most important topics within a wide spectrum of problems in fuzzy sets in general and approximate reasoning, in particular. From the analysis of relevant research it becomes clear that for this purpose, a so-called fuzzy conditional inference rules comes as a viable alternative. In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. For this reason, we put together some comparative analysis of fuzzy reasoning methods in which antecedents contain a conditional proposition with fuzzy concepts and which are based on implication operators present in various types of fuzzy logic. We also show a process of a formation of the fuzzy logic regarded as an algebraic system closed under all its operations. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Continuity and stability features of the formalized rules are investigated.     

结合网络层次分析法的云推理威胁评估模型*

针对传统的威胁评估方法存在指标数据冗余、指标权值设置合理性、推理有效性等问题,建立结合网络层次分析法的云推理威胁评估模型,能够合理精简指标,有效优化推理规则。将该模型用于目标识别系统的威胁评估,首先给出威胁评估指标,用网络层次分析法精简指标并得到规范化权值;构建指标云模型,将规范后的指标数据输入前件云发生器,建立推理规则库,引入分级结构简化规则数,运用加权扎德算子实现规则的合成,将合成结果输入后件云发生器得到威胁度云滴,经多次重复操作后,处理数据得到系统威胁度。最后,以实例说明方法的有效性。     

基于模糊博弈的英式拍卖动态模型

为了给竞价人或其代理的竞价提供决策支持,提出了模糊博弈的英式拍卖动态模型.以模糊参数出价意愿取代估价作为分析的基础,采用Bellman和Zadeh的模糊决策理论替代博弈论中的Nash平衡理论,分析英式拍卖中的竞价行为,建立英式拍卖静态博弈均衡模型,进而提出动态博弈模型和分析动态拍卖策略.通过仿真实验证明算法的有效性.     

Connection Calculus Theorem Proving with Multiple Built-in Theories

Gradually more applications of automated reasoning are discovered. This development has the consequence that deduction systems need to be increasingly flexible. They should exhibit a behavior appropriate to a given problem. One way to achieve this behavior is the integration of different systems or calculi. This leads to the so-called hybrid reasoning (Stickel, 1985; Frisch, 1991; Baumgartner, 1992; Petermann, 1993a) which describes the integration of a general purpose foreground reasoner with one specialized theory reasoner. The aim of this paper is to go a step further, i.e. to treat the theory reasoner as a hybrid system itself. The framework proposed below is suitable for building multiple theories into theorem provers. Those theories can be given syntactically but also semantically. Here, semantical reasoning is understood as reasoning, or rather computing, under a theory given by a class of models, whereas syntactical reasoning means reasoning under a theory given by first-order axioms. The presented approach is a generalization of previous attempts of combining syntactical reasoning under the empty theory with semantical reasoning (Bürckert, 1994; Baumgartner and Stolzenburg, 1995), of combining different theories given syntactically (Petermann, 1997) or just theory (or hybrid) reasoning. The paper formulates sufficient criteria for the construction of complete calculi which enable reasoning under hybrid theories combined from sub-theories given semantically and those given syntactically and briefly reports experimental work.     

The Identification of Job Satisfaction under Z-Information

Complexities in organizational and economical environments have lead psychologists, management scholars, and economists to investigate the multi-dimensional essence of job satisfaction. Unfortunately, existing studies are based on exact data, whereas relevant information is imperfect. To deal with imprecise and partially reliable information, Zadeh proposed the concept of a Z-number. In this paper we consider the Z-number valued rule based model to represent the relationship between job satisfaction and the facets/factors influencing job satisfaction. A real-world job satisfaction index evaluation problem is used to illustrate the suggested approach     

Set-based extended aggregation functions

Inspired by the Zadeh approach to fuzzy connectives in fuzzy set theory and by some applications, we introduce and study set-based extended functions, and in particular, set-based extended aggregation functions. These functions reflect neither reordering nor repetition of input values, and, linking different arities, they introduce serious constraints for extended functions. A complete characterization of set-based extended (aggregation) functions is given, and some constructions of such functions are also proposed, including several examples.     

Internal consistency in plausible reasoning systems

The usual approach to plausible reasoning is to associate a validity measure with each fact or rule, and to compute from these a validity measure for any deduction that is made. This approach is shown to be inappropriate for some classes of problems, particularly those in which the evidence is not internally consistent. Three current plausible reasoning architectures are summarised and each applied to the same small task. An analysis of the performance of these systems reveals deficiencies in each case. The paper then outlines a new approach based on the discovery of consistent subsets of the given evidence. This system can be used either in isolation or in conjunction with a validity-propagating architecture. Comparative results from implementations of all four systems are presented.