1,720,992 research outputs found
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
Get PDFA reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs). The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler).Fuzzy Unification Tree, Dynamic Discrimination of Fuzzy Sets, DKMS, FRCOM
An Expert System for a Business Problem
No full textThe dynamic nature of economic processes and phenomena, their complexity and diversity, the prospect of economic globalization and decentralization, have determined the decision makers to focus on continually improving the methods and techniques aimed to support them, both at the microeconomic and the macroeconomic level. The work in hand compares Business Intelligence Systems (BIS) in relation to Intelligent Business Systems (IBS). We highlight the ways to shift from an E-business system to an IBS system, provide a solution for an Intelligent Business System based on Production Rules (IBSPR). We use this solution in developing a four levels Web application to solve a problem of planning, in compliance with all of the developing phases of an expert system, by using a methodology like UML-Agent for the analysis of the application and by using .Net technology
An Analisys for Intelligent Planning Applications
Get PDFWe investigate some various planning complex applications. We can develop and justify thus a series of modeling and design techniques for intelligent management systems, as well as methods for the analysis of planning systems performance, and, of an expert system in particular, between which there are strong similarities. We will also outline a number of differences between conventional problem solving systems and Intelligent Knowledge Management Systems (IKMSs), the links between expert systems and those of structural and functional planning, the analogy between the model of the problem or business process and the field of the problem represented by a fuzzy knowledge system based on logical events. This way, the work reported in this paper serves to promote the development of some foundations on which to perform careful analysis for intelligent planning systems that operate in critical environments, like Virtual Organizations or Hierarchical Coalitions
Incorporating the Basic Elements of a First-degree Fuzzy Logic and Certain Elments of Temporal Logic for Dynamic Management Applications
Get PDFThe approximate reasoning is perceived as a derivation of new formulas with the corresponding temporal attributes, within a fuzzy theory defined by the fuzzy set of special axioms. For dynamic management applications, the reasoning is evolutionary because of unexpected events which may change the state of the expert system. In this kind of situations it is necessary to elaborate certain mechanisms in order to maintain the coherence of the obtained conclusions, to figure out their degree of reliability and the time domain for which these are true. These last aspects stand as possible further directions of development at a basic logic level. The purpose of this paper is to characterise an extended fuzzy logic system with modal operators, attained by incorporating the basic elements of a first-degree fuzzy logic and certain elements of temporal logic.Dynamic Management Applications, Fuzzy Reasoning, Formalization, Time Restrictions, Modal Operators, Real-Time Expert Decision System (RTEDS)
A Reasoning Algorithm Embedded in a Knowledge Management System
No full textThe importance of this paper consists of demonstrating the possibility of employing an expert Knowledge Management System (KMS) in problems of process control and planning, using imprecise knowledge. It was necessary to continuously adapt known models (e.g. theory of possibilities, discrete event systems) to synthesize a control structure based on fuzzy knowledge. We also tried to conceptually develop a multi-agent real control structure, which is a solution to meet a series of demands on the complexity of the process control. Such systems, especially those based on communication between agents by sharing memory, bring up features well suited for real-time applications, such as: integration of heterogeneous agents, interaction between activities of acquisition, reasoning and action on the external environment, fusion of data coming from sensors of different nature and operation, flexibility and efficiency in the integration of new data needed for reasoning, by simply writing them in the common memory
A BUSINESS SYSTEM BASED ON AGENT TECHNOLOGY DESIGN
No full textThe work in hand compares Business Intelligence Systems (BIS) in relation to Intelligent Business Systems (IBS). We highlight the ways to shift from an E-business system to an IBS system, provide a solution for an Intelligent Business System based on Production Rules (IBSPR). We use this solution in developing a four levels Web application to solve a problem of planning, in compliance with all of the developing phases of an expert system, by using UML-Agent methodology in the analysis of the application and by using .Net technology
An Intelligent Planning System based on Logical Events
No full textThe work reported in this paper serves to promote an Intelligent Planning System that can operate in dynamic and uncertain environments. We can develop and justify thus a series of modelling and design techniques for Intelligent Knowledge Management Systems (IKMS), as well as methods for the analysis of planning systems performance, and, of a fuzzy expert system in particular, between which there are strong similarities. We will also outline a number of differences between conventional problem solving systems and IKMS, the links between expert systems and those of structural and functional planning, the analogy between the model of the problem or process and the problem domain represented by a fuzzy knowledge system based on logical events
SOME CONCEPTUAL PROPERTIES FOR KNOWLEDGE MANAGEMENT SYSTEMS DESIGN
No full textKnowledge Management Systems (KMS) are important tools by which organizations can better useinformation and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) isdifficult to define because it encompasses a range of concepts, management tasks, technologies, and organizationalpractices, all of which come under the umbrella of the information management. Semantic approaches alloweasier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated byrelational databases and document-centric information technologies, procedural algorithmic programmingparadigms, and stack architecture. A key driver of global economic growth in the coming decade is the build-out ofbroadband telecommunications and the deployment of intelligent services bundling. This paper introduces themain characteristics of an Intelligent Knowledge Management System as a multi-agent system used in a LearningControl Problem (IKMSLCP). We describe an intelligent KM framework, allowing the observer (a human agent)to learn from experience
A FORMALISM FOR FUZZY BUSINESS RULES
No full textThe aim of this paper is to provide a formalism for fuzzy rule bases, included in our prototype system FUZZY_ENTERPRISE. This framework can be used in Distributed Knowledge Management Systems (DKMSs), real-time interdisciplinary decision making systems, that often require increasing technical support to high quality decisions in a timely manner. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques
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