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

**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*

**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*

**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*

**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*

**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