Abstract. In this paper, we propose some extensions of epistemic logic for reasoning about information fusion. The fusion operators considered in this paper include majority merging, arbitration, and general merging. Some modalities corresponding to these fusion operators are added to epistemic logics and the Kripke semantics of these extended logics are presented. While most existing approaches treat information fusion operators as meta-level constructs, these operators are directly incorporated into our object logic language. Thus it is possible to reason about not only the merged results but also the fusion process in our logics.

This paper presents an adaptive fuzzy evidential reasoning approach, for segmenting multi-modality MR brain images. A novel fuzzy evidence structure model is proposed under the assumption that each information source provides two types of evidence: probabilistic evidence and fuzzy evidence. A new information measure, called hybrid entropy, is employed for evaluating the overall uncertainty contained in a fuzzy evidence structure. For adaptive reasoning, two discounting strategies are included. To handle conflict between the probabilistic evidence and the fuzzy evidence, local discounting takes into account Kullback-Leibler distance between the two types of evidence. Global discounting takes into account source quality, in terms of Shannon entropy and hybrid entropy, for dealing with conflict of sources. To demonstrate its effectiveness, the approach is applied to segmenting multi-modality MR brain images. It is concluded that the proposed approach performs better than Kmean clustering, majority voting, fuzzy set operators, and Bayesian approach.

This paper extends a conventional discounting scheme commonly used with the Dempster-Shafer evidential reasoning to deal with conflict. The extended discounting scheme is able to augment, discount, and oppose existing evidence structures when discounting factors take values in different ranges. To show its effectiveness, the scheme is employed for detecting multiple sclerosis (MS) lesions based on multi-modality MR images. The approach is fully automated and unsupervised. Experimental results have demonstrated that in addition to the superior segmentation accuracies of brain tissues, good MS detection performances have been obtained (MS detection accuracy 90.28%, similarity index 84.19%, and sensitivity 78.68% on average).

Merging and integrating different requirement specification models which have been developed by domain experts and analysts with dissimilar perspectives on the same issue has been the subject of tremendous amount of research. In this research proposal, we intend to focus on the fact that human analysts ’ opinions possess a degree of uncertainty which can be exploited while integrating conceptual models. We propose an underlying modeling construct which is the basis for transforming conceptual models into a manipulatable format. Based on this construct, we propose to develop methods for negotiating over and merging of conceptual models on top of an extension to the Dempster-Shafer theory of evidence called Subjective logic. The approach shall mainly focus on the formalization of uncertainty and expert reliability through the employment of belief theory. We are also interested in creating a model for pre-consensus negotiation among the involved viewpoints in the conceptual modeling process.

Abstract – The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the efficiency and the generality of this new approach. Keywords: Dezert-Smarandache Theory, DSmT, quantitative and qualitative reasoning, information fusion. MSC 2000: 68T37, 94A15, 94A17, 68T40. 1

Abstract – The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the efficiency and the generality of this new approach. Keywords: Dezert-Smarandache Theory, DSmT, quantitative and qualitative reasoning, information fusion. MSC 2000: 68T37, 94A15, 94A17, 68T40. 1