13,261 research outputs found
Decision-Making with Belief Functions: a Review
Approaches to decision-making under uncertainty in the belief function
framework are reviewed. Most methods are shown to blend criteria for decision
under ignorance with the maximum expected utility principle of Bayesian
decision theory. A distinction is made between methods that construct a
complete preference relation among acts, and those that allow incomparability
of some acts due to lack of information. Methods developed in the imprecise
probability framework are applicable in the Dempster-Shafer context and are
also reviewed. Shafer's constructive decision theory, which substitutes the
notion of goal for that of utility, is described and contrasted with other
approaches. The paper ends by pointing out the need to carry out deeper
investigation of fundamental issues related to decision-making with belief
functions and to assess the descriptive, normative and prescriptive values of
the different approaches
Precise Propagation of Upper and Lower Probability Bounds in System P
In this paper we consider the inference rules of System P in the framework of
coherent imprecise probabilistic assessments. Exploiting our algorithms, we
propagate the lower and upper probability bounds associated with the
conditional assertions of a given knowledge base, automatically obtaining the
precise probability bounds for the derived conclusions of the inference rules.
This allows a more flexible and realistic use of System P in default reasoning
and provides an exact illustration of the degradation of the inference rules
when interpreted in probabilistic terms. We also examine the disjunctive Weak
Rational Monotony of System P+ proposed by Adams in his extended probability
logic.Comment: 8 pages -8th Intl. Workshop on Non-Monotonic Reasoning NMR'2000,
April 9-11, Breckenridge, Colorad
Quality Measures in Uncertain Data Management
Many applications deal with data that is uncertain. Some examples are applications dealing with sensor information, data integration applications and healthcare applications. Instead of these applications having to deal with the uncertainty, it should be the responsibility of the DBMS to manage all data including uncertain data. Several projects do research on this topic. In this paper, we introduce four measures to be used to assess and compare important characteristics of data and systems
Probabilistic Default Reasoning with Conditional Constraints
We propose a combination of probabilistic reasoning from conditional
constraints with approaches to default reasoning from conditional knowledge
bases. In detail, we generalize the notions of Pearl's entailment in system Z,
Lehmann's lexicographic entailment, and Geffner's conditional entailment to
conditional constraints. We give some examples that show that the new notions
of z-, lexicographic, and conditional entailment have similar properties like
their classical counterparts. Moreover, we show that the new notions of z-,
lexicographic, and conditional entailment are proper generalizations of both
their classical counterparts and the classical notion of logical entailment for
conditional constraints.Comment: 8 pages; to appear in Proceedings of the Eighth International
Workshop on Nonmonotonic Reasoning, Special Session on Uncertainty Frameworks
in Nonmonotonic Reasoning, Breckenridge, Colorado, USA, 9-11 April 200
Nonmonotonic Probabilistic Logics between Model-Theoretic Probabilistic Logic and Probabilistic Logic under Coherence
Recently, it has been shown that probabilistic entailment under coherence is
weaker than model-theoretic probabilistic entailment. Moreover, probabilistic
entailment under coherence is a generalization of default entailment in System
P. In this paper, we continue this line of research by presenting probabilistic
generalizations of more sophisticated notions of classical default entailment
that lie between model-theoretic probabilistic entailment and probabilistic
entailment under coherence. That is, the new formalisms properly generalize
their counterparts in classical default reasoning, they are weaker than
model-theoretic probabilistic entailment, and they are stronger than
probabilistic entailment under coherence. The new formalisms are useful
especially for handling probabilistic inconsistencies related to conditioning
on zero events. They can also be applied for probabilistic belief revision.
More generally, in the same spirit as a similar previous paper, this paper
sheds light on exciting new formalisms for probabilistic reasoning beyond the
well-known standard ones.Comment: 10 pages; in Proceedings of the 9th International Workshop on
Non-Monotonic Reasoning (NMR-2002), Special Session on Uncertainty Frameworks
in Nonmonotonic Reasoning, pages 265-274, Toulouse, France, April 200
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