Results 11 - 20
of
29
On ρ in a decision-theoretic apparatus of Dempster-Shafer theory
- International Journal of Approximate Reasoning
, 1995
"... Thomas M. Strat has developed a decision-theoretic apparatus for Dempster-Shafer theory (Decision analysis using belief functions, Intern. J. Approx. Reason. 4(5/6), 391-417, 1990). In this apparatus, expected utility intervals are constructed for different choices. The choice with the highest expec ..."
Abstract
-
Cited by 9 (1 self)
- Add to MetaCart
(Show Context)
Thomas M. Strat has developed a decision-theoretic apparatus for Dempster-Shafer theory (Decision analysis using belief functions, Intern. J. Approx. Reason. 4(5/6), 391-417, 1990). In this apparatus, expected utility intervals are constructed for different choices. The choice with the highest expected utility is preferable to others. However, to find the preferred choice when the expected utility interval of one choice is included in that of another, it is necessary to interpolate a discerning point in the intervals. This is done by the parameter ρ, defined as the probability that the ambiguity about the utility of every nonsingleton focal element will turn out as favorable as possible. If there are several different decision makers, we might sometimes be more interested in having the highest expected utility among the decision makers rather than only trying to maximize our own expected utility regardless of choices made by other decision makers. The preference of each choice is then determined by the probability of yielding the highest expected utility. This probability is equal to the maximal interval length of ρ under which an alternative is preferred. We must here take into account not only the choices already made by other decision makers but also the rational choices we can assume to be made by later decision makers. In Strats apparatus, an assumption, unwarranted by the evidence at hand, has to be made about the value of ρ. We demonstrate that no such assumption is necessary. It is sufficient to assume a uniform probability distribution for ρ to be able to discern the most preferable choice. We discuss when this approach is ustifiable.
An information fusion demonstrator for tactical intelligence processing in network-based defense
, 2007
"... ..."
Clustering decomposed belief functions using generalized weights of conflict
, 2008
"... We develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple su ..."
Abstract
-
Cited by 8 (7 self)
- Add to MetaCart
We develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple support and inverse simple support functions that are clustered based on their pairwise generalized weights of conflict, constrained by weights of attraction assigned to keep track of all decompositions. The generalized conflict c 2 ð 1; 1Þ and generalized weight of conflict J 2 ð 1; 1Þ are derived in the combination of simple support and inverse simple support functions.
Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure
- Proceedings of the 1999 Information, Decision and Control Conference (IDC'99)
, 1999
"... In this paper we extend an earlier result within Dempster-Shafer theory ["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU’98)] where several pieces of eviden ..."
Abstract
-
Cited by 7 (7 self)
- Add to MetaCart
In this paper we extend an earlier result within Dempster-Shafer theory ["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU’98)] where several pieces of evidence were clustered into a fixed number of clusters using a neural structure. This was done by minimizing a metaconflict function. We now develop a method for simultaneous clustering and determination of number of clusters during iteration in the neural structure. We let the output signals of neurons represent the degree to which a pieces of evidence belong to a corresponding cluster. From these we derive a probability distribution regarding the number of clusters, which gradually during the iteration is transformed into a determination of number of clusters. This gradual determination is fed back into the neural structure at each iteration to influence the clustering process.
Managing Decomposed Belief Functions
"... In this paper we develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions ..."
Abstract
-
Cited by 6 (5 self)
- Add to MetaCart
In this paper we develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple support and inverse simple support functions that are clustered based on their pairwise generalized weights of conflict, constrained by weights of attraction assigned to keep track of all decompositions. The generalized conflict c ∈ (−∞, ∞) and generalized weight of conflict J − ∈ (−∞, ∞) are derived in the combination of simple support and inverse simple support functions.
Methods and system design of the IFD03 information fusion demonstrator
- Proceedings of the Ninth International Command and Control Research and Technology Symposium (9th ICCRTS)
, 2004
"... The Swedish Defence Research Agency has developed a concept demonstrator for demonstrating information fusion methodology focused on intelligence processing at the division level for a future Network Based Defence (NBF) / Network Centric Warfare (NCW) C4ISR system. The demonstrator integrates force ..."
Abstract
-
Cited by 6 (4 self)
- Add to MetaCart
The Swedish Defence Research Agency has developed a concept demonstrator for demonstrating information fusion methodology focused on intelligence processing at the division level for a future Network Based Defence (NBF) / Network Centric Warfare (NCW) C4ISR system. The demonstrator integrates force aggregation, particle filtering and sensor allocation methods to construct, dynamically update and maintain a situation picture.
The IFD03 Information Fusion Demonstrator -- requirements, methodology, design, and experiences
, 2004
"... ..."
Constructing Multiple Frames of Discernment for Multiple Subproblems
"... Abstract.In this paper we extend a methodology for constructing a frame of discernment from belief functions for one problem, into a methodology for constructing multiple frames of discernment for several different subproblems. The most appropriate frames of discernment are those that let our eviden ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Abstract.In this paper we extend a methodology for constructing a frame of discernment from belief functions for one problem, into a methodology for constructing multiple frames of discernment for several different subproblems. The most appropriate frames of discernment are those that let our evidence interact in an interesting way without exhibit too much internal conflict. A function measuring overall frame appropriateness is mapped onto a Potts spin neural network in order to find the partition of all belief functions that yields the most appropriate frames. Keywords: Dempster-Shafer theory, belief function, representation, frame of discernment, clustering, Potts spin, conflict, simulated annealing. 1
Contents lists available at ScienceDirect International Journal of Approximate Reasoning
"... journal homepage: www.elsevier.com/locate/ijar Conflict management in Dempster–Shafer theory using the degree ..."
Abstract
- Add to MetaCart
(Show Context)
journal homepage: www.elsevier.com/locate/ijar Conflict management in Dempster–Shafer theory using the degree
Issues of Uncertainty Analysis in High-Level Information Fusion
"... Abstract High-Level Information Fusion (HLIF) utilizes techniques from Low-Level Information Fusion (LLIF) to support situation/impact assessment, user involvement, and mission and resource management (SUM). Given the unbounded analysis of situations, events, users, resources, and missions; it is ob ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract High-Level Information Fusion (HLIF) utilizes techniques from Low-Level Information Fusion (LLIF) to support situation/impact assessment, user involvement, and mission and resource management (SUM). Given the unbounded analysis of situations, events, users, resources, and missions; it is obvious that uncertainty is manifested by the nature of application requirements. In this panel, we seek discussions on methods and techniques to intelligently assess the problem of HLIF uncertainty analysis to alleviate high-performance statistical computational optimizations, unrealizable mathematical assumptions, or rigorous modeling and problem scoping which lead to time delays, brittleness, and rigidity, respectively. Given the various methods of LLIF and the complexity of HLIF, an interest to the ISIF community is to utilize diverse methods (such as those from other communities) that bridge the LLIF-HLIF gap of uncertainty analysis. To get a qualified and diverse viewpoint, we present a summary of HLIF uncertainty processes towards developing a multisource ontology of uncertainty to support HLIF modeling, methods, and management and systems design.
