Human reasoning usually is very approximate and involves various types of uncertainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, rough set, near set and hybrid i...

This chapter presents a general overview and foundations of the DSmT, i.e. the recent theory of plausible and paradoxical reasoning developed by the authors, specially for the static or dynamic fusion of information arising from several independent but potentially highly conflicting, uncertain and imprecise sources of evidence. We introduce and justify here the basis of the DSmT framework with ...

We d iscuss a methodology f o r hand l ing u n c e r t a i n i n f o r m a t i o n i n exper t and o ther i n t e l l i g e n t systems. Th is approach combines the t h e o r i e s of approximate reasoning and Dempster— Shafer .

Belief maintenance represents a unified approach to assumption-based and numerical uncertainty management. A formal equivalence is demonstrated between Shafer-Dempster belief theory and assumption-based truth maintenance extended to incorporate a probability calculus on assumptions. Belief propagation through truth maintenance automatically and correctly accounts for non-independencies among pr...

Several logics for reasoning under uncertainty distribute probabil ity mass over sets in some sense These include probabilistic logic Dempster Shafer theory other logics based on belief functions and second order probabilistic logic We show that these logics are in stances of a certain type of linear programming model typically with exponentially many variables We also show how a single linear ...

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for pos...

In this paper we present the research results in the field of perception for real microrobotic swarm. The proposed hardware and software solution uses IR-based reflective measurement for individual perception and the Dampster-Shafer evidential reasoning for hypothesis refinement in collective perception. Especial attention is paid to a reliable identification of encountered geometries and a red...

This paper presents the case for using the Dempster-Shafer theory of evidence and a derivative of it, to model the reasoning process in debates on difficult philosophical, theological and scientific questions. This gives a useful formal framework within which to enhance the debating process. A well-known theological debate and two scientific exemplars are given for illustration of the working a...

This chapter presents a general overview and foundations of the DSmT, i.e. the recent theory of plausible and paradoxical reasoning developed by the authors, specially for the static or dynamic fusion of information arising from several independent but potentially highly conflicting, uncertain and imprecise sources of evidence. We introduce and justify here the basis of the DSmT framework with ...

This paper addresses the problem of multi-criteria recommendation in hotel industry. The main focus is to analyze user preferences from different aspects based on ratings and develop a new collaborative filtering method for recommendations. Particularly, proposed system integrates matrix factorization into deep learning model predict ratings, then evidential reasoning approach adopted uncertain...