A method for knowledge representation with automatic - uncertainty embodiment

Campos, Fabio; Cavalcante, Sílvia

doi:10.1109/nlpke.2003.1275876

Cited by 6 publications

(4 citation statements)

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“…Or, likewise: m2 () = 0.14 Note that the second person preferred not stating anything about the possibility "d"; and as he did not divide 100% of (13) his beliefs among the possibilities, the remaining (0.14) was assigned to e.…”

Section: E Belief Intervalmentioning

confidence: 99%

“…OUR APPROACH The proposed rule derates the beliefs according to the degree of conflict between the evidence, assigning the remaining belief to the environment (and not to the common hypothesis) along with the uncertainty that would be assigned to the environment by the original Dempster's Rule [13]. This quantity of belief assigned to the environment constitutes a measure of the subjective uncertainty coming from the lack of knowledge or conflict among the evidence, being named "Lateo", and denoted by A, in allusion to its causes, once "Lateo" in Latin means "being hidden", "being out of sight", "be unknown".…”

Section: Analyzing the Counter Intuitive Behaviormentioning

confidence: 99%

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Decision Making under Subjective Uncertainty

Campos

Neves

Souza

2007

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

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The uncertainty may be classified into two major groups, "objective uncertainty" and "subjective uncertainty". The subject of this article is the decision making under subjective uncertainty. One of the formal models that deal with subjective uncertainty, the Mathematical Theory of Evidence, is extended and its counter-intuitive behavior corrected, allowing the making of correct decisions in a wider range of situations than the original model.The Mathematical Theory of Evidence, or Dempster-Shafer Theory, is a popular formalism to model someone's degrees of belieL This theory provides a method for combining evidence from different sources without prior knowledge of their distributions, it is also possible to assign probability values to sets of possibilities rather than to single events only, and it is unnecessary to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. However, it has some pitfalls caused by the non-natural embodiment of the uncertainty in the results.In this paper we present a method of automatic embodiment of the uncertainty that overcomes the aforementioned pitfalls, allowing the combination of evidence with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypotheses. This is accomplished by means of a new rule of combination of bodies of evidence that embodies in the numeric results the unknown belief and conflict among the evidence, naturally modeling the epistemic reasoning.

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Section: E Belief Intervalmentioning

confidence: 99%

Section: Analyzing the Counter Intuitive Behaviormentioning

confidence: 99%

Decision Making under Subjective Uncertainty

Campos

Neves

Souza

2007

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

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“…3 Counter intuitive behavior of the combination rules A classical problem [9], [10], [13] with the Combination Rules used until now is a counter intuitive result found when the evidence to be combined have a concentration of belief in elements disjoint between them, and a common element with low degrees of belief assigned to it. Because the rules do not include any intrinsic mean of belief derating, proportionally to the amount of uncertainty (coming from the conflict among them), they can assign 100% of belief to the element less believed but common to the evidence.…”

Section: Weight Of Conflictmentioning

confidence: 99%

“…The proposed rule derates the beliefs according to the degree of conflict between the evidence, assigning the remaining belief to the environment (and not to the common hypothesis) along with the uncertainty that would be assigned to the environment by the original Dempster's Rule [3]. This quantity ofbelief assigned to the environment constitutes a measure ofthe subjective uncertainty coming from the non knowledge or conflict among the evidence, being named "Lateo" and denoted by A, in allusion to its causes, once "Lateo" in Latin means "being hidden", "being out of sight", "be unknown".…”

Section: Our Approachmentioning

confidence: 99%

Dealing with Subjective Uncertainty in Knowledge Based Systems

Campos

Neves

Neto

2007

2007 IEEE Symposium on Computers and Communications

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Knowledge based systems depend on algorithms able to relate the inputs ofa system to a correct answer coming out ofthe knowledge-base. Practical systems shows that imperfect information will always get into the data-base and an imperfect knowledge-base will always exist, thus it is usual for a knowledge based system has to be able to model and deal with information imperfections.The kind of information imperfections involved in knowledge-base systems can be modeled by the "subjective uncertainty", which with the "objective uncertainty" compose the dual nature of the uncertainty, a taxonomy first defined by Helton.One of the formal models that deals with subjective uncertainty is the Mathematical Theory of Evidence, or Dempster-Shafer Theory. This theory provides a method for combining evidencefrom different sources withoutprior knowledge of their distributions, however, it has some pitfalls caused by the non natural embodiment of the uncertainty in the results.In this paper we analyze the counter-intuitive behavior of the theory, identify the sources of the subjective uncertainty, and present a method of automatic embodiment of the uncertainty which overcomes the pitfalls, allowing its use in a broad range ofsituations.

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