Artificial Intelligence

1005 Submissions

[5] viXra:1005.0080 [pdf] submitted on 20 May 2010

Multi-Criteria Decision Making Based on DSmT-Ahp

Authors: Jean Dezert, Jean-Marc Tacnet, Mireille Batton-Hubert, Florentin Smarandache
Comments: 6 pages

In this paper, we present an extension of the multicriteria decision making based on the Analytic Hierarchy Process (AHP) which incorporates uncertain knowledge matrices for generating basic belief assignments (bba's). The combination of priority vectors corresponding to bba's related to each (sub)-criterion is performed using the Proportional Conflict Redistribution rule no. 5 proposed in Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. The method presented here, called DSmT-AHP, is illustrated on very simple examples.
Category: Artificial Intelligence

[4] viXra:1005.0079 [pdf] submitted on 20 May 2010

Non Bayesian Conditioning and Deconditioning

Authors: Jean Dezert, Florentin Smarandache
Comments: 6 pages

In this paper, we present a Non-Bayesian conditioning rule for belief revision. This rule is truly Non-Bayesian in the sense that it doesn't satisfy the common adopted principle that when a prior belief is Bayesian, after conditioning by X, Bel(X|X) must be equal to one. Our new conditioning rule for belief revision is based on the proportional conflict redistribution rule of combination developed in DSmT (Dezert-Smarandache Theory) which abandons Bayes' conditioning principle. Such Non-Bayesian conditioning allows to take into account judiciously the level of conflict between the prior belief available and the conditional evidence. We also introduce the deconditioning problem and show that this problem admits a unique solution in the case of Bayesian prior; a solution which is not possible to obtain when classical Shafer and Bayes conditioning rules are used. Several simple examples are also presented to compare the results between this new Non-Bayesian conditioning and the classical one.
Category: Artificial Intelligence

[3] viXra:1005.0077 [pdf] submitted on 19 May 2010

Fusion of Masses Defined on Infinite Countable Frames of Discernment

Authors: Florentin Smarandache, Arnaud Martin
Comments: 5 pages

In this paper we introduce for the first time the fusion of information on infinite discrete frames of discernment and we give general results of the fusion of two such masses using the Dempster's rule and the PCR5 rule for Bayesian and non-Bayesian cases.
Category: Artificial Intelligence

[2] viXra:1005.0076 [pdf] submitted on 19 May 2010

Degree of Uncertainty of a Set and of a Mass

Authors: Florentin Smarandache, Arnaud Martin
Comments: 9 pages

In this paper we use extend Harley's measure of uncertainty of a set and of mass to the degree of uncertainty of a set and of a mass (bba).
Category: Artificial Intelligence

[1] viXra:1005.0044 [pdf] submitted on 11 Mar 2010

Fuzzy Interval Matrices, Neutrosophic Interval Matrices and Their Applications

Authors: W. B. Vasantha Kandasamy, Florentin Smarandache
Comments: 304 pages

The new concept of fuzzy interval matrices has been introduced in this book for the first time. The authors have not only introduced the notion of fuzzy interval matrices, interval neutrosophic matrices and fuzzy neutrosophic interval matrices but have also demonstrated some of its applications when the data under study is an unsupervised one and when several experts analyze the problem. Further, the authors have introduced in this book multiexpert models using these three new types of interval matrices. The new multi expert models dealt in this book are FCIMs, FRIMs, FCInMs, FRInMs, IBAMs, IBBAMs, nIBAMs, FAIMs, FAnIMS, etc. Illustrative examples are given so that the reader can follow these concepts easily. This book has three chapters. The first chapter is introductory in nature and makes the book a self-contained one. Chapter two introduces the concept of fuzzy interval matrices. Also the notion of fuzzy interval matrices, neutrosophic interval matrices and fuzzy neutrosophic interval matrices, can find applications to Markov chains and Leontief economic models. Chapter three gives the application of fuzzy interval matrices and neutrosophic interval matrices to real-world problems by constructing the models already mentioned. Further these models are mainly useful when the data is an unsupervised one and when one needs a multi-expert model. The new concept of fuzzy interval matrices and neutrosophic interval matrices will find their applications in engineering, medical, industrial, social and psychological problems. We have given a long list of references to help the interested reader.
Category: Artificial Intelligence