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Decision Analytic Networks in Artificial Intelligence
, 1995
"... Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a fa ..."
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Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a family of graphical models of decision theory known as influence diagrams or as belief networks. These models are equally attractive to theoreticians, decision modelers, and designers of knowledgebased systems. From a theoretical perspective, they combine graph theory, probability theory and decision theory. From an implementation perspective, they lead to powerful automated systems. Although many practicing decision analysts have already adopted influence diagrams as modeling and structuring tools, they may remain unaware of the theoretical work that has emerged from the artificial intelligence community. This paper surveys the first decade or so of this work. Investment Technology Group, ...
Evaluating Uncertainty Representation and Reasoning
 in HLF systems,” Int. Conference on Information Fusion
, 2011
"... Abstract—Highlevel fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLF framework must support aut ..."
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Abstract—Highlevel fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLF framework must support automated knowledge representation and reasoning with uncertainty, there is no consensus on the most appropriate technology to satisfy this requirement. Further, the debate among proponents of the various approaches is laden with miscommunication and illsupported assumptions, which inhibits advancement of HLF research as a whole. A clearly defined, scientifically rigorous evaluation framework is needed to help information fusion researchers assess the suitability of various approaches and tools to their applications. This paper describes requirements for such a framework and describes a use case in HLF evaluation.
On the Behavior of Dempster’s Rule of Combination and the Foundations of DempsterShafer Theory,” (Best paper award
 in Proc. of IEEE IS’2012
"... Abstract—On the base of simple emblematic example we analyze and explain the inconsistent and inadequate behavior of DempsterShafer’s rule of combination as a valid method to combine sources of evidences. We identify the cause and the effect of the dictatorial power behavior of this rule and of its ..."
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Abstract—On the base of simple emblematic example we analyze and explain the inconsistent and inadequate behavior of DempsterShafer’s rule of combination as a valid method to combine sources of evidences. We identify the cause and the effect of the dictatorial power behavior of this rule and of its impossibility to manage the conflicts between the sources. For a comparison purpose, we present the respective solution obtained by the more efficient PCR5 fusion rule proposed originally in DezertSmarandache Theory framework. Finally, we identify and prove the inherent contradiction of DempsterShafer Theory foundations. Keywords—Belief functions; DempsterShafer Theory; DSmT; PCR5; contradiction.
Imprecise probabilities with a generalized interval form
 Proc. 3rd Int. Workshop on Reliability Engineering Computing (REC'08
, 2008
"... Abstract. Di erent representations of imprecise probabilities have been proposed, such as behavioral theory, evidence theory, possibility theory, probability bound analysis, Fprobabilities, fuzzy probabilities, and clouds. These methods use intervalvalued parameters to discribe probability distrib ..."
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Abstract. Di erent representations of imprecise probabilities have been proposed, such as behavioral theory, evidence theory, possibility theory, probability bound analysis, Fprobabilities, fuzzy probabilities, and clouds. These methods use intervalvalued parameters to discribe probability distributions such that uncertainty is distinguished from variability. In this paper, we proposed a new form of imprecise probabilities based on generalized or modal intervals. Generalized intervals are algebraically closed under Kaucher arithmetic, which provides a concise representation and calculus structure as an extension of precise probabilities. With the separation between proper and improper interval probabilities, focal and nonfocal events are di erentiated based on the modalities and logical semantics of generalized interval probabilities. Focal events have the semantics of critical, uncontrollable, speci ed, etc. in probabilistic analysis, whereas the corresponding nonfocal events are complementary, controllable, and derived. A generalized imprecise conditional probability is de ned based on unconditional interval probabilities such that the algebraic relation between conditional and marginal interval probabilities is maintained. A Bayes ' rule with generalized intervals (GIBR) is also proposed. The GIBR allows us to interpret the logic relationship between interval prior and posterior probabilities.
The Assumptions Behind Dempster's Rule
 Proceedings of the Ninth Conference of Uncertainty in Articial Intelligence (UAI93
, 1993
"... This paper examines the concept of a combination rule for belief functions. It is shown that two fairly simple and apparently reasonable assumptions determine Dempster’s rule, giving a new justification for it. ..."
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This paper examines the concept of a combination rule for belief functions. It is shown that two fairly simple and apparently reasonable assumptions determine Dempster’s rule, giving a new justification for it.
A BeliefTheoretic Reputation Estimation Model for MultiContext Communities
"... Abstract. Online communities have grown to be an alternative form of communication for many people. This widespread growth and influence of the members of these communities in shaping the desire, line of thought and behavior of each other requires subtle mechanisms that are often easily attainable i ..."
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Abstract. Online communities have grown to be an alternative form of communication for many people. This widespread growth and influence of the members of these communities in shaping the desire, line of thought and behavior of each other requires subtle mechanisms that are often easily attainable in facetoface communications. In this paper, we address a special case of the trustmaking process, where a person needs to make a judgment about the propositions, capabilities, or truthfulness of another community member where none of the community members has had any previous interaction with. Our proposed model estimates the possible reputation of a given identity in a new context by observing his/her behavior in the perspective of the other contexts of the community. This is most important for websites such as amazon.com, ebay.com, epinions.com, etc whose activities encompass multiple domains. Our proposed model employs DempsterShafer based valuation networks to represent a global reputation structure and performs a belief propagation technique to infer contextual reputation. The evaluation of the model on a dataset collected from epinions.com shows promising results. 1
On the mathematical theory of evidence and Dempster’s rule of combination, May 2011(http://hal. archivesouvertes.fr/hal00591633/fr/). ha l0 3, v er sio n  1 Se p
"... Abstract—In this paper we present an analysis of the use of Dempster’s rule of combination, its consistency with the probability calculus and its usefulness for combining sources of evidence expressed by belief functions in the framework of the Mathematical Theory of Evidence, known also as Dempster ..."
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Abstract—In this paper we present an analysis of the use of Dempster’s rule of combination, its consistency with the probability calculus and its usefulness for combining sources of evidence expressed by belief functions in the framework of the Mathematical Theory of Evidence, known also as DempsterShafer Theory (DST), or as the classical theory of belief functions. We show that the direct combination of original basic belief assignments of sources of evidence with Dempster’s rule is inconsistent with probability calculus and we explain this from a simple experimental protocol. We also show how Dempster’s rule can be reconciled with probability calculus for such examples if the combination of sources is done differently. In that case the notion of conflict between sources of evidence becomes meaningless (it just vanishes) because Dempster’s rule coincides with the pure conjunctive operator, and the entire fusion process becomes comparable to simple application of Total Probability Theorem (i.e. the weighted average fusion rule). While a direct application of Dempster’s rule becomes questionable in the most general case, then arises the need of a methodoloy for organizing and implementing the combination rules with respect to the applications.
An Evidential Reasoning Based Decision Making Process for Prequalifying Construction Contractors
"... An evidential reasoning (ER) approach is applied to evaluate prequalification criteria in selection of a main contractor. This approach has proved to be useful and practical when a decision problem under consideration includes multiple criteria, which are of both a quantitative and qualitative nat ..."
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An evidential reasoning (ER) approach is applied to evaluate prequalification criteria in selection of a main contractor. This approach has proved to be useful and practical when a decision problem under consideration includes multiple criteria, which are of both a quantitative and qualitative nature. In this type of problem, a major difficulty is how to aggregate numerical data and subjective assessments in order to evaluate potential alternative contractors. A contractor prequalification problem (CPP) is investigated to show how the ER approach can overcome this difficulty. The decision making process is fully explained using the ER approach together with discussions about the advantages and disadvantages of the model presented.
Why Dempster’s fusion rule is not a generalization of Bayes fusion rule
"... Abstract—In this paper, we analyze Bayes fusion rule in details from a fusion standpoint, as well as the emblematic Dempster’s rule of combination introduced by Shafer in his Mathematical Theory of evidence based on belief functions. We propose a new interesting formulation of Bayes rule and point o ..."
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Abstract—In this paper, we analyze Bayes fusion rule in details from a fusion standpoint, as well as the emblematic Dempster’s rule of combination introduced by Shafer in his Mathematical Theory of evidence based on belief functions. We propose a new interesting formulation of Bayes rule and point out some of its properties. A deep analysis of the compatibility of Dempster’s fusion rule with Bayes fusion rule is done. We show that Dempster’s rule is compatible with Bayes fusion rule only in the very particular case where the basic belief assignments (bba’s) to combine are Bayesian, and when the prior information is modeled either by a uniform probability measure, or by a vacuous bba. We show clearly that Dempster’s rule becomes incompatible with Bayes rule in the more general case where the prior is truly informative (not uniform, nor vacuous). Consequently, this paper proves that Dempster’s rule is not a generalization of Bayes fusion rule. Keywords—Information fusion, Probability theory, Bayes fusion rule, Dempster’s fusion rule.
The Combination of Different Pieces of Evidence Using Incidence Calculus
 Dept. of Artificial Intelligence, Univ. of Edinburgh
, 1992
"... Combining multiple sources of information is a major and difficult task in the management of uncertainty. Dempster's combination rule is one of the attractive approaches. However, many researchers have pointed out that the application domains of the rule are rather limited and it sometimes give ..."
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Combining multiple sources of information is a major and difficult task in the management of uncertainty. Dempster's combination rule is one of the attractive approaches. However, many researchers have pointed out that the application domains of the rule are rather limited and it sometimes gives unexpected results. In this paper, we have further explored the nature of combination and achieved the following main results. 1). The condition of combination in Dempster's original combination framework is more strict than that required by Dempster's combination rule in DempsterShafer theory of evidence. 2). Some counterintuitive results of using Dempster's combination rule shown in some papers are caused by the overlooking (or ignorance) of different independence conditions required by Dempster's original combination framework and Dempster's combination rule. 3). In Dempster's combination rule, combinations are performed at the target information level. This rule itself does not provide a c...