Fuzzy logic and the Dempster-Shafer theory of evidence is applied to an IS multiattribute decision making problem whereby the project manager must select project team members from candidates, none of whom may exactly ...
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Fuzzy logic and the Dempster-Shafer theory of evidence is applied to an IS multiattribute decision making problem whereby the project manager must select project team members from candidates, none of whom may exactly satisfy the ideal level of skills needed at any point in time. the decision mechanism is constrained by the uncertainty inherent in the determination of the relative importance of each skill and the classification of potential team members. this latter uncertainty of potential team membership is addressed through expert evaluation of the degree to which each potential ream member possesses each skill. then the belief and plausibility that a candidate will satisfy the decision maker's ideal skill levels are calculated and combined to rank order the available candidates. the changing skill requirements are addressed through an iterative process for each project phase.
Default reasoning and approximate reasoning can both be handled in the framework of possibility theory. this article first restates and contrasts the two approaches. then a preliminary discussion of the modelling of f...
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Default reasoning and approximate reasoning can both be handled in the framework of possibility theory. this article first restates and contrasts the two approaches. then a preliminary discussion of the modelling of fuzzy default rules is provided. A fuzzy default rule is a default rule, such as "small birds fly", or "flights are usually not much delayed", whose expression involves gradual properties.
Chu spaces, a formalism used to describe parallelism and information flow, provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations, of membership functions,...
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Chu spaces, a formalism used to describe parallelism and information flow, provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations, of membership functions, of defuzzification, etc. Chu spaces provide a uniform justification for various aspects of fuzzy methodology that will help in solving important problems related to this methodology.
Addresses the modeling of chaotic systems using fuzzy functional approximations. In numerical simulations of chaos, local modeling errors are cumulative. A natural question, therefore, arises in examining a numericall...
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Addresses the modeling of chaotic systems using fuzzy functional approximations. In numerical simulations of chaos, local modeling errors are cumulative. A natural question, therefore, arises in examining a numerically generated chaotic trajectory: is there a "true" trajectory of the chaotic system, starting from a nearby set of initial conditions, that shadows, for a certain /spl epsiv/ distance, the numerically generated one? this paper extends the literature on fuzzy chaos by introducing concepts from chaos theory, such as /spl delta/-pseudo-orbits and /spl epsiv/-shadowing, to the language of fuzzy approximation theory. It presents corollaries to the fuzzy approximation theorem and the SAM theorem (B. Kosko, 1997) that describe sufficiency conditions for additive fuzzy logic systems to generate /spl delta/-pseudo-orbits that are /spl epsiv/-shadowed by true orbits of the dynamic process.
Safety and reliability analyses often depend on Boolean logic combinations of input variables that have uncertainty (imperfect knowledge) or variability (probabilistically described outcomes). Calculating safety and r...
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Safety and reliability analyses often depend on Boolean logic combinations of input variables that have uncertainty (imperfect knowledge) or variability (probabilistically described outcomes). Calculating safety and reliability probabilities with functions of uncertain variables can yield incorrect or misleading results if some precautions are not taken. One important consideration is the application of constrained mathematics for calculating probabilities for functions that contain repeated variables. An example of a constraint is that an uncertain variable that appears multiple times in a Boolean expression must always have the same value, although the value cannot be exactly specified. It has been recognized that using interval-based computations such as interval arithmetic and fuzzy or possibilistic mathematics in an unconstrained mode (applied by sequentially parsing equation solutions), and even Monte Carlo analysis can significantly misrepresent extreme values. this phenomenon, its ramifications, and a solution for the problem are discussed.
In his recent paper `Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems', L. Zadeh explains that probability theory needs an infusion of fuzzy logic to enha...
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In his recent paper `Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems', L. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with real-world problems. In this talk, we give an example of a real-world problem for which such an infusion is indeed successful: the problem of system reliability.
this paper describes the specification-based testing, analysis tools, and associated processes that were used to independently validate, verify, and ultimately provide for certifying, safety-critical software develope...
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this paper describes the specification-based testing, analysis tools, and associated processes that were used to independently validate, verify, and ultimately provide for certifying, safety-critical software developed for the Traffic Alert and Collision Avoidance System (TCAS II) program. these tools and processes comprise an effective and Independent Validation and Verification (IV&V) activity applied to the Collision Avoidance Subsystem (CAS) software development process. A requirements specification language called the Requirements State Machine Language (RSML), originally developed by the University of California, Irvine (UCI), was employed for the specification of CAS. the end result is the next generation of TCAS II collision avoidance logic, referred to as Version 7, that is of a higher quality than its predecessors, meets the certification requirements of DO-178B Level B, and can be shown to satisfy the new operational requirements it was developed to address.
In this paper, we examine how to adequately engineer an information system that is guaranteed to produce high quality information. We also characterize the dimensions of information quality from the perspective of a s...
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In this paper, we examine how to adequately engineer an information system that is guaranteed to produce high quality information. We also characterize the dimensions of information quality from the perspective of a systems designer. this relates directly with our view that information is an engineered product coming out of the information manufacturing processing system. Further, we integrate fuzzy sets/logic concepts into our model to facilitate the characterization of the continuum of information quality since information quality is highly contextual and dependent on the user.
One of the objectives of Artificial Intelligence has been the modelling of `human' characteristics, such as emotions, behaviour, conscience, etc. But in such characteristics we might find certain degree of contrad...
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One of the objectives of Artificial Intelligence has been the modelling of `human' characteristics, such as emotions, behaviour, conscience, etc. But in such characteristics we might find certain degree of contradiction. Previous work on modelling emotions and its problems are reviewed. A model for emotions is proposed using multidimensional logic, which handles the degree of contradiction that emotions might have. the model is oriented to simulate emotions in artificial societies. the proposed solution is also generalized for actions which might overcome contradiction (conflictive goals in agents, for example.).
this paper describes a fuzzy expert case-based reasoning system. the idea is to combine methodologies from both technologies to come up with a system that utilizes inference procedures with matching algorithms used by...
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this paper describes a fuzzy expert case-based reasoning system. the idea is to combine methodologies from both technologies to come up with a system that utilizes inference procedures with matching algorithms used by case-based reasoning systems. We describe the system and, with examples, show how it can be utilized to solve problems in a more natural way than some of the existing case-based reasoning systems.
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