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Gregory Provan - One of the best experts on this subject based on the ideXlab platform.

  • A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Gregory Provan
    Abstract:

    We formulate Dempster Shafer Belief functions in terms of Propositional Logic using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of propositional clauses, assigning weights to certain propositional literals enables the Belief functions to be explicitly computed using Network Reliability techniques. Also, the logical procedure corresponding to updating Belief functions using Dempster's Rule of Combination is shown. This analysis formalizes the implementation of Belief functions within an Assumption-based Truth Maintenance System (ATMS). We describe the extension of an ATMS-based visual recognition system, VICTORS, with this logical formulation of Dempster Shafer Theory. Without Dempster Shafer Theory, VICTORS computes all possible visual interpretations (i.e. all logical models) without determining the best interpretation(s). Incorporating Dempster Shafer Theory enables optimal visual interpretations to be computed and a logical semantics to be maintained.

  • A logic-based analysis of Dempster-Shafer Theory
    International Journal of Approximate Reasoning, 1990
    Co-Authors: Gregory Provan
    Abstract:

    We formulate Dempster Shafer Theory in terms of Propositional Logic, using the implicit notion of probability underlying Dempster Shafer Theory. Dempster Shafer Theory can be modeled in terms of propositional logic by the tuple $(\Sigma , \varrho )$, where $\Sigma $ is a set of propositional clauses and $\varrho $ is an assignment of measure to each clause $\Sigma_i \in \Sigma $. We show that the disjunction of minimal support clauses for a clause $i$ with respect to a set $\Sigma $ of propositional clauses, $ \xi ( \Sigma_{i}, \Sigma )$, is a symbolic representation of the Dempster Shafer Belief function for $\Sigma_{i}$. The combination of Belief functions using Dempster''s Rule of Combination corresponds to a combination of the corresponding support clauses. The disjointness of the Boolean formulae representing DS Belief functions is shown to be necessary. Methods of computing disjoint formulae using Network Reliability techniques are discussed. \n In addition, we explore the computational complexity of deriving Dempster Shafer Belief functions, including that of the logic-based methods which are the focus of this paper. Because of Intractability even for moderately-sized problem instances, we propose the use of effluent approximation methods for such computations. Finally, we examine implementations of Dempster Shafer Theory, based on domain restrictions of DS Theory, hypertree embeddings, and the ATMS.

  • UAI - The Application of Dempster Shafer Theory to a Logic-Based Visual Recognition System
    Uncertainty in Artificial Intelligence, 1990
    Co-Authors: Gregory Provan
    Abstract:

    Abstract We formulate Dempster Shafer Belief functions in terms of Propositional Logic, using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of propositional clauses, assigning weights to certain propositional literals enables the Belief functions to be explicitly computed using Network Reliability techniques. Also, the logical procedure corresponding to updating Belief functions using Dempster's Rule of Combination is shown. This analysis formalizes the implementation of Belief functions within an Assumption-based Truth Maintenance System (ATMS). We describe the extension of an ATMS-based visual recognition system, VICTORS, with this logical formulation of Dempster Shafer Theory. Without Dempster Shafer Theory, VICTORS computes all possible visual interpretations (i.e. all logical models) without determining the best interpretation(s). Incorporating Dempster Shafer Theory enables optimal visual interpretations to be computed and a logical semantics to be maintained.

Mahmud Hasan - One of the best experts on this subject based on the ideXlab platform.

  • Combining Fuzzy Logic and Dempster-Shafer Theory
    Indonesian Journal of Electrical Engineering and Computer Science, 2015
    Co-Authors: Andino Maseleno, Mahmud Hasan, N. J. Tuah
    Abstract:

    This research aims to combine the mathematical Theory of evidence with the rule based logics to refine the predictable output. Integrating Fuzzy Logic and Dempster-Shafer Theory by calculating the similarity between Fuzzy membership function. The novelty aspect of this work is that basic probability assignment is proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get a basic probability assignment. The Dempster-Shafer mathematical Theory of evidence has attracted considerable attention as a promising method of dealing with some of the basic problems arising in combination of evidence and data fusion. Dempster-Shafer Theory provides the ability to deal with ignorance and missing information. The foundation of Fuzzy logic is natural language which can help to make full use of expert information. Full Text: PDF DOI: http://dx.doi.org/10.11591/telkomnika.v16i3.9370

  • Avian influenza (H5N1) expert system using Dempster-Shafer Theory
    International Journal of Information and Communication Technology, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    Based on cumulative number of confirmed human cases of avian influenza (H5N1) reported to World Health Organization (WHO) in 2011 from 15 countries, Indonesia has the largest number of deaths because of avian influenza which 146 deaths. In this research, the researcher built an avian influenza (H5N1) expert system for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Dempster-Shafer Theory is to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveals that avian influenza (H5N1) expert system has successfully identified the existence of avian influenza and displaying the result of identification process.

  • African Trypanosomiasis Detection using Dempster-Shafer Theory
    arXiv: Artificial Intelligence, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    World Health Organization reports that African Trypanosomiasis affects mostly poor populations living in remote rural areas of Africa that can be fatal if properly not treated. This paper presents Dempster-Shafer Theory for the detection of African trypanosomiasis. Sustainable elimination of African trypanosomiasis as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we implement Dempster-Shafer Theory for detecting African trypanosomiasis and displaying the result of detection process. We describe eleven symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, bleeding around the bite, joint the paint, swollen lymph nodes, sleep disturbances, meningitis and arthritis. Dempster-Shafer Theory to quantify the degree of belief, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.

  • Skin Diseases Expert System using Dempster-Shafer Theory
    International Journal of Intelligent Systems and Applications, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer Theory to quantify the degree of belief, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.

  • Avian Influenza (H5N1) Expert System using Dempster-Shafer Theory
    arXiv: Artificial Intelligence, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    Based on Cumulative Number of Confirmed Human Cases of Avian Influenza (H5N1) Reported to World Health Organization (WHO) in the 2011 from 15 countries, Indonesia has the largest number death because Avian Influenza which 146 deaths. In this research, the researcher built an Avian Influenza (H5N1) Expert System for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Research location is in the Lampung Province, South Sumatera. The researcher reason to choose Lampung Province in South Sumatera on the basis that has a high poultry population. Dempster-Shafer Theory to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Avian Influenza (H5N1) Expert System has successfully identified the existence of avian influenza and displaying the result of identification process.

Andino Maseleno - One of the best experts on this subject based on the ideXlab platform.

  • Combining Fuzzy Logic and Dempster-Shafer Theory
    Indonesian Journal of Electrical Engineering and Computer Science, 2015
    Co-Authors: Andino Maseleno, Mahmud Hasan, N. J. Tuah
    Abstract:

    This research aims to combine the mathematical Theory of evidence with the rule based logics to refine the predictable output. Integrating Fuzzy Logic and Dempster-Shafer Theory by calculating the similarity between Fuzzy membership function. The novelty aspect of this work is that basic probability assignment is proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get a basic probability assignment. The Dempster-Shafer mathematical Theory of evidence has attracted considerable attention as a promising method of dealing with some of the basic problems arising in combination of evidence and data fusion. Dempster-Shafer Theory provides the ability to deal with ignorance and missing information. The foundation of Fuzzy logic is natural language which can help to make full use of expert information. Full Text: PDF DOI: http://dx.doi.org/10.11591/telkomnika.v16i3.9370

  • the dempster shafer Theory algorithm and its application to insect diseases detection
    2013
    Co-Authors: Andino Maseleno
    Abstract:

    This paper presents Dempster-Shafer Theory for insect diseases detection. Sustainable elimination of insect diseases as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we used Dempster-Shafer Theory for detecting insect diseases and displaying the result of detection process. Insect diseases which include babesiosis, dengue fever, lyme, malaria, and west nile. We describe six symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, and arthritis. Dempster-Shafer Theory to quantify the degree of belief, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.

  • Avian influenza (H5N1) expert system using Dempster-Shafer Theory
    International Journal of Information and Communication Technology, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    Based on cumulative number of confirmed human cases of avian influenza (H5N1) reported to World Health Organization (WHO) in 2011 from 15 countries, Indonesia has the largest number of deaths because of avian influenza which 146 deaths. In this research, the researcher built an avian influenza (H5N1) expert system for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Dempster-Shafer Theory is to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveals that avian influenza (H5N1) expert system has successfully identified the existence of avian influenza and displaying the result of identification process.

  • African Trypanosomiasis Detection using Dempster-Shafer Theory
    arXiv: Artificial Intelligence, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    World Health Organization reports that African Trypanosomiasis affects mostly poor populations living in remote rural areas of Africa that can be fatal if properly not treated. This paper presents Dempster-Shafer Theory for the detection of African trypanosomiasis. Sustainable elimination of African trypanosomiasis as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we implement Dempster-Shafer Theory for detecting African trypanosomiasis and displaying the result of detection process. We describe eleven symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, bleeding around the bite, joint the paint, swollen lymph nodes, sleep disturbances, meningitis and arthritis. Dempster-Shafer Theory to quantify the degree of belief, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.

  • Skin Diseases Expert System using Dempster-Shafer Theory
    International Journal of Intelligent Systems and Applications, 2012
    Co-Authors: Andino Maseleno, Mahmud Hasan
    Abstract:

    Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer Theory to quantify the degree of belief, our approach uses Dempster-Shafer Theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.

David Harmanec - One of the best experts on this subject based on the ideXlab platform.

  • Toward a Characterization of Uncertainty Measure for the Dempster-Shafer Theory
    arXiv: Artificial Intelligence, 2013
    Co-Authors: David Harmanec
    Abstract:

    This is a working paper summarizing results of an ongoing research project whose aim is to uniquely characterize the uncertainty measure for the Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented, some of their implications are shown, and the proof is given of the minimality of recently proposed measure AU among all measures satisfying the proposed requirements.

  • Uncertainty in Dempster-Shafer Theory
    1997
    Co-Authors: David Harmanec
    Abstract:

    This dissertation is a major step toward the development of a well-founded Theory of uncertainty in Dempster-Shafer Theory. A new measure of uncertainty in Dempster-Shafer Theory is proposed. The measure, denoted AU, is defined as the maximum of the Shannon entropy on the set of all probabilities dominating a given belief function. It is proven that this measure satisfies all the basic properties of a reasonable measure of uncertainty, most notably the property of subadditivity. Since the new measure is defined as a solution to a non-linear optimization problem, it was necessary for any potential applications of the measure to find an efficient algorithm for computing it. Such an algorithm is presented in the dissertation, both for the general case and for the special case of possibility Theory. The correctness of the two versions of the algorithm is proven. As a step toward a proof of uniqueness, it is proven that the measure AU is the smallest measure among all measures (if any other exists) satisfying several intuitive axioms. As a development of the general principle of uncertainty invariance, uncertainty invariant and consistent transformations between belief functions, probability measures, and possibility measures are investigated. The requirement of uncertainty invariance generally does not guarantee a unique solution. The measure of nonspecificity is used as a useful secondary guidance criterion to resolve this indeterminacy. The relationship between uncertainty and combination of evidence by the Dempster rule of combination in Dempster-Shafer Theory is studied. As a result of this study, new notions of conflict and information gain are proposed. Finally, directions for further research are discussed.

  • ON THE COMPUTATION OF UNCERTAINTY MEASURE IN Dempster-Shafer Theory
    International Journal of General Systems, 1996
    Co-Authors: David Harmanec, George J. Klir, Germano Resconi, Yin Pan
    Abstract:

    An algorithm for computing the recently proposed measure of uncertainly AU for Dempster-Shafer Theory is presented. The correctness of the algorithm is proven. The algorithm is illustrated by simple examples. Some implementation issues are also discussed.

  • MEASURING TOTAL UNCERTAINTY IN Dempster-Shafer Theory: A NOVEL APPROACH
    International Journal of General Systems, 1994
    Co-Authors: David Harmanec, George J. Klir
    Abstract:

    A novel approach to measuring uncertainty and uncertainty-based information in Dempster-Shafer Theory is proposed (independently also proposed by Maeda et al. [1993]). It is shown that the proposed measure of total uncertainty in Dempster-Shafer Theory is both additive and subadditive, has a desired range, and collapses correctly to either the Shannon entropy or the Hartley measure of uncertainty for special probability assignment functions. The paper is restricted, for the sake of simplicity, to finite sets.

  • On modal logic interpretation of Dempster–Shafer Theory of evidence
    International Journal of Intelligent Systems, 1994
    Co-Authors: David Harmanec, George J. Klir, Germano Resconi
    Abstract:

    This article further develops one branch of research initiated in an article by Resconi, Klir, and St. Clair (G. Resconi, G. J. Klir, and U. St. Clair, Int. J. Gen. Syst., 21(1), 23-50 (1992) and continued in another article by Resconi et al. (Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, 1(1), 1993). It fully formulates an interpretation of the Dempster-Shafer Theory in terms of the standard semantics of modal logic. It is shown how to represent the basic probability assignment function as well as the commonality function of the Dempster-Shafer Theory by modal logic and that this representation is complete for rational-valued functions (basic assignment, belief, or plausibility functions). © 1994 John Wiley & Sons, Inc.

George J. Klir - One of the best experts on this subject based on the ideXlab platform.

  • ON THE COMPUTATION OF UNCERTAINTY MEASURE IN Dempster-Shafer Theory
    International Journal of General Systems, 1996
    Co-Authors: David Harmanec, George J. Klir, Germano Resconi, Yin Pan
    Abstract:

    An algorithm for computing the recently proposed measure of uncertainly AU for Dempster-Shafer Theory is presented. The correctness of the algorithm is proven. The algorithm is illustrated by simple examples. Some implementation issues are also discussed.

  • MEASURING TOTAL UNCERTAINTY IN Dempster-Shafer Theory: A NOVEL APPROACH
    International Journal of General Systems, 1994
    Co-Authors: David Harmanec, George J. Klir
    Abstract:

    A novel approach to measuring uncertainty and uncertainty-based information in Dempster-Shafer Theory is proposed (independently also proposed by Maeda et al. [1993]). It is shown that the proposed measure of total uncertainty in Dempster-Shafer Theory is both additive and subadditive, has a desired range, and collapses correctly to either the Shannon entropy or the Hartley measure of uncertainty for special probability assignment functions. The paper is restricted, for the sake of simplicity, to finite sets.

  • On modal logic interpretation of Dempster–Shafer Theory of evidence
    International Journal of Intelligent Systems, 1994
    Co-Authors: David Harmanec, George J. Klir, Germano Resconi
    Abstract:

    This article further develops one branch of research initiated in an article by Resconi, Klir, and St. Clair (G. Resconi, G. J. Klir, and U. St. Clair, Int. J. Gen. Syst., 21(1), 23-50 (1992) and continued in another article by Resconi et al. (Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, 1(1), 1993). It fully formulates an interpretation of the Dempster-Shafer Theory in terms of the standard semantics of modal logic. It is shown how to represent the basic probability assignment function as well as the commonality function of the Dempster-Shafer Theory by modal logic and that this representation is complete for rational-valued functions (basic assignment, belief, or plausibility functions). © 1994 John Wiley & Sons, Inc.

  • Measures of discord in the Dempster-Shafer Theory
    Information Sciences, 1993
    Co-Authors: Arthur Ramer, George J. Klir
    Abstract:

    Abstract This paper is a companion of a previous paper, in which a new measure of uncertainty, called a measure of discord, was introduced. While the previous paper focuses on intuitive justification of this new measure and identification of deficiencies of some previously considered measures, this paper is oriented toward a mathematical consideration of the measure of discord. Also discussed in the paper is a total measure of uncertainty in the Dempster-Shafer Theory, defined as the sum of discord and nonspecificity.