On the semantics of rule-based expert systems with uncertainty

Kifer, Michael; Ai, Li

doi:10.1007/3-540-50171-1_6

Cited by 52 publications

(41 citation statements)

References 16 publications

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“…We model an observer's background knowledge (BK obs ) as a set of probabilistic first-order Datalog (pDatalog) clauses [25]. pDatalog is much more expressive than propositional logic variations usually used in inference control on statistical databases [30] in that it allows us to model relationships among attributes as well as relationships between attributes and time.…”

Section: Background Knowledge Modelmentioning

confidence: 99%

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Middleware for Pervasive Spaces: Balancing Privacy and Utility

et al. 2009

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Abstract. Middleware for pervasive spaces has to meet conflicting requirements. It has to both maximize the utility of the information exposed and ensure that this information does not violate users' privacy. In order to resolve these conflicts, we propose a framework grounded in utility theory where users dynamically control the level of disclosure about their information. We begin by providing appropriate definitions of privacy and utility for the type of applications that would support collaborative work in an office environment-current definitions of privacy and anonymity do not apply in this context. We propose a distributed solution that, given a user's background knowledge, maximizes the utility of the information being disclosed to information recipients while meeting the privacy requirements of users. We implement our solution in the context of a real pervasive space middleware and provide experiments that demonstrate its behaviour.

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Section: Background Knowledge Modelmentioning

confidence: 99%

“…The time complexity of this first step is polynomial because we make the following assumptions on the background knowledge model. We assume that uncertainty functions (f ) adhere to the "natural restrictions" [25] of monotonicity (f (…”

Section: A Simulated Annealing Based Solutionmentioning

confidence: 99%

Middleware for Pervasive Spaces: Balancing Privacy and Utility

et al. 2009

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“…Additionally, practical considerations dictate that the framework used for knowledge representation with uncertainty admit efficient implementation and efficient computations. Logic database programming, with its advantage of modularity and its powerful top-down and bottom-up query processing techniques, has attracted the attention of researchers and numerous frameworks for deductive databases with uncertainty have been proposed [2, 4, 5, 9, 10, 14-16, 18-22, 25-27, 35-39], where the underlying uncertainty formalism include probability theory [10,19,22,[25][26][27]39], fuzzy set theory [2,35,37,38], multi-valued logic [9,15,16,20,21] and possibilistic logic [5]. Roughly, based on the way in which uncertainty is associated with the facts and rules of a program, these frameworks can be classified into annotation based (AB) and implication based (IB).…”

Section: Introductionmentioning

confidence: 99%

The Well-Founded Semantics in Normal Logic Programs with Uncertainty

Loyer

Straccia

2002

Lecture Notes in Computer Science

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Abstract. Many frameworks of logic programming have been proposed to manage uncertain information in deductive databases and expert systems. Roughly, on the basis of how uncertainty is associated to facts and the rules in a program, they can be classified into implication-based (IB) and annotation-based (AB). However, one fundamental issue that remains unaddressed in the IB approach is the representation and the manipulation of the non-monotonic mode of negation, an important feature for real applications. Our focus in this paper is to introduce nonmonotonic negation in the parametric IB framework, a unifying umbrella for IB frameworks. The semantical approach that we will adopt is based on the well-founded semantics, one of the most widely studied and used semantics of (classical) logic programs with negation.

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“…The problem of uncertainty management in logic programs has attracted the attention of many researchers and numerous frameworks have been proposed [1-3, 6, 8-11, 13-17]. Each of them addresses the management of different kind of uncertainty: (i) probability theory [6,10,[13][14][15]; (ii) fuzzy set theory [1,16,17]; (iii) multi-valued logic [8,9,11]; and (iv) possibilistic logic [3]. Apart from the different notion of uncertainty they rely on, these frameworks differ in the way in which uncertainty is associated with the facts and rules of a program.…”

Section: Introductionmentioning

confidence: 99%

“…Here f is an n-ary computable function and β i is either a constant or a variable ranging over an appropriate certainty domain. Examples of AB frameworks include [8,9,14,15]. In the IB approach, a rule is of the form A α ← B 1 , ..., B n , which says that the certainty associated with the implication B 1 ∧ ... ∧ B n → A is α. Computationally, given an assignment v of certainties to the B i s, the certainty of A is computed by taking the "conjunction" of the certainties v(B i ) and then somehow "propagating" it to the rule head.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and Partial Non-uniform Assumptions in Parametric Deductive Databases

Loyer

Straccia

2002

Logics in Artificial Intelligence

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Abstract. Different many-valued logic programming frameworks have been proposed to manage uncertain information in deductive databases and logic programming. A feature of these frameworks is that they rely on a predefined assumption or hypothesis, i.e. an interpretation that assigns the same default truth value to all the atoms of a program, e.g. in the open world assumption, by default all atoms have unknown truth value. In this paper we extend these frameworks along three directions: (i) we will introduce non-monotonic modes of negation; (ii) the default truth values of atoms need not necessarily to be all equal each other; and (iii) a hypothesis can be a partial interpretation. We will show that our approach extends the usual ones: if we restrict our attention to classical logic programs and consider total uniform hypotheses, then our semantics reduces to the usual semantics of logic programs. In particular, under the everything false assumption, our semantics captures and extends the well-founded semantics to these frameworks.

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On the semantics of rule-based expert systems with uncertainty

Cited by 52 publications

References 16 publications

Middleware for Pervasive Spaces: Balancing Privacy and Utility

Middleware for Pervasive Spaces: Balancing Privacy and Utility

The Well-Founded Semantics in Normal Logic Programs with Uncertainty

Uncertainty and Partial Non-uniform Assumptions in Parametric Deductive Databases

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