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Conjunctive and Disjunctive Combination of Belief Functions Induced by Non Distinct Bodies of Evidence
 ARTIFICIAL INTELLIGENCE
, 2007
"... Dempster’s rule plays a central role in the theory of belief functions. However, it assumes the combined bodies of evidence to be distinct, an assumption which is not always verified in practice. In this paper, a new operator, the cautious rule of combination, is introduced. This operator is commuta ..."
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Cited by 34 (10 self)
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Dempster’s rule plays a central role in the theory of belief functions. However, it assumes the combined bodies of evidence to be distinct, an assumption which is not always verified in practice. In this paper, a new operator, the cautious rule of combination, is introduced. This operator is commutative, associative and idempotent. This latter property makes it suitable to combine belief functions induced by reliable, but possibly overlapping bodies of evidence. A dual operator, the bold disjunctive rule, is also introduced. This operator is also commutative, associative and idempotent, and can be used to combine belief functions issues from possibly overlapping and unreliable sources. Finally, the cautious and bold rules are shown to be particular members of infinite families of conjunctive and disjunctive combination rules based on triangular norms and conorms.
Shedding new light on Zadeh’s criticism of Dempster’s rule of combination
 In Proc. 8th Int. Conf. Information Fusion, contribution No. C81
, 2005
"... Abstract – Shortly after proposing Dempster’s rule of combination as a general aggregation rule for evidence from independent sources, Zadeh formulated an annoying example for which Dempster’s rule seemed to produce counterintuitive results. Since then, many authors have used Zadeh’s example either ..."
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Cited by 18 (0 self)
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Abstract – Shortly after proposing Dempster’s rule of combination as a general aggregation rule for evidence from independent sources, Zadeh formulated an annoying example for which Dempster’s rule seemed to produce counterintuitive results. Since then, many authors have used Zadeh’s example either to criticize DemsterShafer theory as a whole, or as a motivation for constructing alternative combination rules. This paper shows that the counterintuition of Zadeh’s example is not a problem of Dempster’s rule, but a problem of Zadeh’s model that does not correspond to reality. Two different ways to fix the problem will be discussed, showing that Dempster’s rule of combination perfectly behaves as one would expect. 1,2
Sensor Data Fusion for ContextAware Computing Using DempsterShafer Theory
, 2003
"... Towards having computers understand human users context information, this dissertation proposes a systematic contextsensing implementation methodology that can easily combine sensor outputs with subjective judgments. The feasibility of this idea is demonstrated via a meetingparticipants focusofa ..."
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Cited by 17 (0 self)
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Towards having computers understand human users context information, this dissertation proposes a systematic contextsensing implementation methodology that can easily combine sensor outputs with subjective judgments. The feasibility of this idea is demonstrated via a meetingparticipants focusofattention analysis case study with several simulated sensors using prerecorded experimental data and artificially generated sensor outputs distributed over a LAN network. The methodology advocates a topdown approach: (1) For a given application, a context information structure is defined
The AdvisorPOMDP: A Principled Approach to Trust through Reputation in Electronic Markets
 In PST
, 2005
"... This paper examines approaches to representing uncertainty in reputation systems for electronic markets with the aim of constructing a decision theoretic framework for collecting information about selling agents and making purchase decisions in the context of a social reputation system. A selection ..."
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Cited by 13 (2 self)
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This paper examines approaches to representing uncertainty in reputation systems for electronic markets with the aim of constructing a decision theoretic framework for collecting information about selling agents and making purchase decisions in the context of a social reputation system. A selection of approaches to representing reputation using DempsterShafter Theory and Bayesian probability are surveyed and a model for collecting and using reputation is developed using a Partially Observable Markov Decision Process.
Extensions of belief functions and possibility distributions by using the imprecise Dirichlet model. Fuzzy Sets and Systems
, 2005
"... A belief function can be viewed as a generalized probability function and the belief and plausibility measures can be regarded as lower and upper bounds for the probability of an event. From this point of view, the common approach for computing belief and plausibility measures may be unreasonable in ..."
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Cited by 9 (5 self)
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A belief function can be viewed as a generalized probability function and the belief and plausibility measures can be regarded as lower and upper bounds for the probability of an event. From this point of view, the common approach for computing belief and plausibility measures may be unreasonable in many real applications because there are cases where the probability function that governs the random process is not exactly known. In order to overcome this difficulty, Walley’s imprecise Dirichlet model is used to extend the belief, plausibility and possibility measures. An interesting relationship between belief measures and sets of multinomial models is established. A combination rule taking into account reliability of sources of data is proposed. Various numerical examples illustrate the proposed extension.
Robust Bayesianism: Imprecise and Paradoxical Reasoning
, 2004
"... We are interested in understanding the relationship between Bayesian inference and evidence theory, in particular imprecise and paradoxical reasoning. The concept of a set of probability distributions is central both in robust Bayesian analysis and in some versions of DempsterShafer theory. Most of ..."
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Cited by 6 (1 self)
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We are interested in understanding the relationship between Bayesian inference and evidence theory, in particular imprecise and paradoxical reasoning. The concept of a set of probability distributions is central both in robust Bayesian analysis and in some versions of DempsterShafer theory. Most of the literature regards these two theories as incomparable. We interpret imprecise probabilities as imprecise posteriors obtainable from imprecise likelihoods and priors, both of which can be considered as evidence and represented with, e.g., DSstructures. The natural and simple robust combination operator makes all pairwise combinations of elements from the two sets. The DSstructures can represent one particular family of imprecise distributions, Choquet capacities. These are not closed under our combination rule, but can be made so by rounding. The proposed combination operator is unique, and has interesting normative and factual properties. We compare its behavior on Zadeh's example with other proposed fusion rules. We also show how the paradoxical reasoning method appears in the robust framework.
Policy recognition for multiplayer tactical scenarios
 In AAMAS ’07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
, 2007
"... This paper addresses the problem of recognizing policies given logs of battle scenarios from multiplayer games. The ability to identify individual and team policies from observations is important for a wide range of applications including automated commentary generation, game coaching, and opponent ..."
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Cited by 6 (1 self)
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This paper addresses the problem of recognizing policies given logs of battle scenarios from multiplayer games. The ability to identify individual and team policies from observations is important for a wide range of applications including automated commentary generation, game coaching, and opponent modeling. We define a policy as a preference model over possible actions based on the game state, and a team policy as a collection of individual policies along with an assignment of players to policies. This paper explores two promising approaches for policy recognition: (1) a modelbased system for combining evidence from observed events using DempsterShafer theory, and (2) a datadriven discriminative classifier using support vector machines (SVMs). We evaluate our techniques on logs of real and simulated games played using Open Gaming Foundation d20, the rule system used by many popular tabletop games, including
Prioritizing Intrusion Analysis Using DempsterShafer Theory
"... Intrusion analysis and incident management remains a difficult problem in practical network security defense. The root cause of this problem is the large rate of false positives in the sensors used by Intrusion Detection System (IDS) systems, reducing the value of the alerts to an administrator. Sta ..."
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Cited by 4 (3 self)
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Intrusion analysis and incident management remains a difficult problem in practical network security defense. The root cause of this problem is the large rate of false positives in the sensors used by Intrusion Detection System (IDS) systems, reducing the value of the alerts to an administrator. Standard Bayesian theory has not been effective in this regard because of the lack of good prior knowledge. This paper presents an approach to handling such uncertainty without the need for prior information, through the DempsterShafer (DS) theory. We address a number of practical but fundamental issues in applying DS to intrusion analysis, including how to model sensors ’ trustworthiness, where to obtain such parameters, and how to address the lack of independence among alerts. We present an efficient algorithm for carrying out DS belief calculation on an IDS alert correlation graph, so that one can compute a belief score for a given hypothesis, e.g. a specific machine is compromised. The belief strength can be used to sort incidentrelated hypotheses and prioritize further analysis by a human analyst of the hypotheses and the associated evidence. We have implemented our approach for the opensource IDS system Snort and evaluated its effectiveness on a number of data sets as well as a production network. The resulting belief scores were verified through both anecdotal experience on the production system as well as by comparing the belief rankings of hypotheses with the ground truths provided by the data sets we used in evaluation, showing thereby that belief scores can be effective in mitigating the high false positive rate problem in intrusion analysis.
Possibilitybased design optimization method for design problems with both statistical and fuzzy input data
 Journal of Mechanical Design
, 2006
"... The reliability based design optimization (RBDO) method is prevailing in stochastic structural design optimization by assuming the amount of input data is sufficient enough to create accurate input statistical distribution. If the sufficient input data cannot be generated due to limitations in techn ..."
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Cited by 4 (0 self)
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The reliability based design optimization (RBDO) method is prevailing in stochastic structural design optimization by assuming the amount of input data is sufficient enough to create accurate input statistical distribution. If the sufficient input data cannot be generated due to limitations in technical and/or facility resources, the possibilitybased design optimization (PBDO) method can be used to obtain reliable designs by utilizing membership functions for epistemic uncertainties. For RBDO, the performance measure approach (PMA) is well established and accepted by many investigators. It is found that the same PMA is very much desirable approach also for the PBDO problems. In many industry design problems, we have to deal with the statistical random and fuzzy input variables simultaneously. For the design problem with both statistical random and fuzzy input variables, it is not desirable to use RBDO since it could lead to an unreliable optimum design. This paper proposes to use PBDO for design optimization for such problems. For fuzzy variables, several methods for membership function generation are proposed. As less detailed information is available for the input data, the membership function that provides more conservative optimum design should be selected. For statistical random variable, the membership function that yields least conservative optimum design is proposed by using the possibilityprobability consistency theory and the least conservative condition. The proposed approach for design problems with mixed type input variables is applied to some example problems to
Trusted collaborative spectrum sensing for mobile cognitive radio networks
 32nd IEEE International Conference on Computer Communications, INFOCOM
, 2012
"... Abstract—Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). It is inaccurate if spectrum sensing nodes are malicious. Although mobility is an inherent property of wireless networks, there has been no prior work studying the detection of malicious users for collabo ..."
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Abstract—Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). It is inaccurate if spectrum sensing nodes are malicious. Although mobility is an inherent property of wireless networks, there has been no prior work studying the detection of malicious users for collaborative spectrum sensing in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at locations with severe pathloss. In this paper, we propose to use two trust parameters, Location Reliability and Malicious Intention (LRMI), to improve malicious and primary user detection in mobile CRNs under attacks. Location Reliability reflects pathloss characteristics of the wireless channel and Malicious Intention captures the true intention of secondary users, respectively. Simulations of our proposed detection mechanisms, LRMI, show that mobility helps train location reliability and detect malicious users. We show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20 % at false alarm rate of 5%, respectively. I.