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Planning and Reacting in Uncertain and Dynamic Environments
, 1995
"... Agents situated in dynamic and uncertain environments require several capabilities for successful operation. Such agents must monitor the world and respond appropriately to important events. The agents should be able to accept goals, synthesize complex plans for achieving those goals, and execute th ..."
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Cited by 120 (12 self)
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Agents situated in dynamic and uncertain environments require several capabilities for successful operation. Such agents must monitor the world and respond appropriately to important events. The agents should be able to accept goals, synthesize complex plans for achieving those goals, and execute the plans while continuing to be responsive to changes in the world. As events render some current activities obsolete, the agents should be able to modify their plans while continuing activities unaffected by those events. The Cypress system is a domainindependent framework for defining persistent agents with this full range of behavior. Cypress has been used for several demanding applications, including military operations, realtime tracking, and fault diagnosis. ii Contents 1 Introduction 1 1.1 Research Strategy : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 A New Technology : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 2 Overview of C...
Perspectives on the Theory and Practice of Belief Functions
 International Journal of Approximate Reasoning
, 1990
"... The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answer ..."
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Cited by 101 (7 self)
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The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answers to that question. The theory of belief functions is more flexible; it allows us to derive degrees of belief for a question from probabilities for a related question. These degrees of belief may or may not have the mathematical properties of probabilities; how much they differ from probabilities will depend on how closely the two questions are related. Examples of what we would now call belieffunction reasoning can be found in the late seventeenth and early eighteenth centuries, well before Bayesian ideas were developed. In 1689, George Hooper gave rules for combining testimony that can be recognized as special cases of Dempster's rule for combining belief functions (Shafer 1986a). Similar rules were formulated by Jakob Bernoulli in his Ars Conjectandi, published posthumously in 1713, and by JohannHeinrich Lambert in his Neues Organon, published in 1764 (Shafer 1978). Examples of belieffunction reasoning can also be found in more recent work, by authors
Soft Computing: the Convergence of Emerging Reasoning Technologies
 Soft Computing
, 1997
"... The term Soft Computing (SC) represents the combination of emerging problemsolving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to so ..."
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Cited by 68 (11 self)
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The term Soft Computing (SC) represents the combination of emerging problemsolving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, realworld problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagationtype algorithms.
Approximation Algorithms and Decision Making in the DempsterShafer Theory of Evidence  An Empirical Study
 International Journal of Approximate Reasoning
, 1996
"... The computational complexity of reasoning within the DempsterShafer theory of evidence is one of the major points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief ..."
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Cited by 61 (0 self)
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The computational complexity of reasoning within the DempsterShafer theory of evidence is one of the major points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief functions involved. This article reviews a number of algorithms based on this method and introduces a new onethe D1 algorithmthat was designed to bring about minimal deviations in those values that are relevant to decision making. It describes an empirical study that examines the appropriateness of these approximation procedures in decisionmaking situations. It presents and interprets the empirical findings along several dimensions and discusses the various tradeoffs that have to be taken into account when actually applying one of these methods. 2 1. Introduction The complexity of the computations that have to be carried out in the DempsterShafer theory of evidence (DST) [3, 10] i...
Reasoning with Belief Functions: An Analysis of Compatibility
 International Journal of Approximate Reasoning
, 1990
"... This paper examines the applicability of belief functions methodology in three reasoning tasks: (1) representation of incomplete knowledge, (2) belief updating, and (3) evidence pooling. We find that belief functions have difficulties representing incomplete knowledge, primarily knowledge expressed ..."
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Cited by 41 (0 self)
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This paper examines the applicability of belief functions methodology in three reasoning tasks: (1) representation of incomplete knowledge, (2) belief updating, and (3) evidence pooling. We find that belief functions have difficulties representing incomplete knowledge, primarily knowledge expressed in conditional sentences. In this context, we also show that the prevailing practices of encoding ifthen rules as belief function expressions are inadequate, as they lead to counterintuitive conclusions under chaining, contraposition, and reasoning by cases. Next, we examine the role of belief functions in updating states of belief and find that, if partial knowledge is encoded and updated by belief function methods, the updating process violates basic patterns of plausibility and the resulting beliefs cannot serve as a basis for rational decisions. Finally, assessing their role in evidence pooling, we find that belief functions offer a rich language for describing the evidence gathered, highly compatible with the way people summarize observations. However, the methods available for integrating evidence into a coherent state of belief ca
On nonspecific evidence
 International Journal of Intelligent Systems
, 1993
"... When simultaneously reasoning with evidences about several different events it is necessary to separate the evidence according to event. These events should then be handled independently. However, when propositions of evidences are weakly specified in the sense that it may not be certain to which ev ..."
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Cited by 29 (24 self)
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When simultaneously reasoning with evidences about several different events it is necessary to separate the evidence according to event. These events should then be handled independently. However, when propositions of evidences are weakly specified in the sense that it may not be certain to which event they are referring, this may not be directly possible. In this paper a criterion for partitioning evidences into subsets representing events is established. This criterion, derived from the conflict within each subset, involves minimising a criterion function for the overall conflict of the partition. An algorithm based on characteristics of the criterion function and an iterative optimisation among partitionings of evidences is proposed.
Coarsening Approximations of Belief Functions
 In Proc. of ECSQARU'2001 (to appear
, 2001
"... A method is proposed for reducing the size of a frame of discernment, in such a way that the loss of information content in a set of belief functions is minimized. This approach allows to compute strong inner and outer approximations which can be combined efficiently using the Fast Möbius Transform ..."
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Cited by 20 (0 self)
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A method is proposed for reducing the size of a frame of discernment, in such a way that the loss of information content in a set of belief functions is minimized. This approach allows to compute strong inner and outer approximations which can be combined efficiently using the Fast Möbius Transform algorithm.
Geometric Analysis of Belief Space and Conditional Subspaces
, 2001
"... In this paper the geometric structure of the space S of the belief functions dened over a discrete set (be lief space) is analyzed. Using the Moebius inversion lemma we prove the recursive bundle structure of the belief space and show how an arbitrary belief function can be uniquely represented ..."
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Cited by 19 (18 self)
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In this paper the geometric structure of the space S of the belief functions dened over a discrete set (be lief space) is analyzed. Using the Moebius inversion lemma we prove the recursive bundle structure of the belief space and show how an arbitrary belief function can be uniquely represented as a convex combination of certain elements of the bers, giving S the form of a simplex. The commutativity of orthogonal sum and convex closure operator is proved and used to depict the geometric structure of conditional subspaces, i.e. sets of belief functions conditioned by a given function s. Future applications of these geometric methods to classical problems like probabilistic approximation and canonical decomposition are outlined.