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27
An argumentation framework for merging conflicting knowledge bases: The prioritized case
 In Technical report. Artois University, CRIL
, 2005
"... Abstract. An important problem in the management of knowledgebased systems is the handling of inconsistency. Inconsistency may appear because the knowledge may come from different sources of information. To solve this problem, two kinds of approaches have been proposed. The first category merges ..."
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Abstract. An important problem in the management of knowledgebased systems is the handling of inconsistency. Inconsistency may appear because the knowledge may come from different sources of information. To solve this problem, two kinds of approaches have been proposed. The first category merges the different bases into a unique base, and the second category of approaches, such as argumentation, accepts inconsistency and copes with it. Recently, a “powerful ” approach [7, 8, 13] has been proposed to merge prioritized propositional bases encoded in possibilistic logic. This approach consists of combining prioritized knowledge bases into a new prioritized knowledge base, and then to infer from this last. In this paper, we present a particular argumentation framework for handling inconsistency arised from the presence of multiple sources of information. Then, we will show that this framework retrieves the results of the merging operator defined in [7, 8, 13]. Moreover, we will show that an argumentationbased approach palliates the limits, due to the drowning problem, of the merging operator.
Extending Description Logics with Uncertainty Reasoning in Possibilistic Logic
, 2007
"... Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics as an extension of description logics. We give semantics and syntax of possibilistic description logics. We then define two inference se ..."
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Cited by 22 (4 self)
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Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics as an extension of description logics. We give semantics and syntax of possibilistic description logics. We then define two inference services in possibilistic description logics. Since possibilistic inference suffers from the drowning problem, we consider a drowningfree variant of possibilistic inference, called linear order inference. Finally, we implement the algorithms for inference services in possibilistic description logics using KAON2 reasoner.
A stratificationbased approach for handling conflicts in access control
 In 8th ACM Symposium on Access Control Models and Technologies (SACMAT’03
, 2003
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A Stratificationbased Approach for Inconsistency Handling in Description Logics
"... Abstract. Inconsistency handling is a central problem in many knowledge representation fields, such as belief revision, belief merging. Many approaches have been proposed to handle inconsistency in ontologies. In this paper, we propose a stratificationbased approach for inconsistency handling in de ..."
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Cited by 9 (2 self)
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Abstract. Inconsistency handling is a central problem in many knowledge representation fields, such as belief revision, belief merging. Many approaches have been proposed to handle inconsistency in ontologies. In this paper, we propose a stratificationbased approach for inconsistency handling in description logics (DLs), a family of ontology languages. This approach consists of two steps. In the first step, we obtain a preference relation on the axioms in the DL knowledge base using an algorithm. Then two existing approaches in firstorder logic are adapted to resolve conflicting information in the stratified DL knowledge base. 1
A possibilistic extension of description logics
 In Proc. of DL’07, 2007
"... Abstract. Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics (DLs) as an extension of description logics. We give semantics and syntax of possibilistic description logics. Two kinds of inf ..."
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Cited by 9 (1 self)
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Abstract. Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics (DLs) as an extension of description logics. We give semantics and syntax of possibilistic description logics. Two kinds of inference services are considered in our logics and algorithms are provided for them. These algorithms are implemented using KAON2 reasoner. 1
A SplitCombination Approach to Merging Knowledge Bases in Possibilistic Logic
, 2006
"... In this paper, we propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: one is called the splitting step and the other is called the combination step. T ..."
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In this paper, we propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: one is called the splitting step and the other is called the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, di#erent classes of subbases are combined using di#erent operators. Our approach is applied to knowledge bases which are selfconsistent and the result of merging is also a consistent knowledge base. Two operators are proposed based on two di#erent splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the tconorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntaxbased merging operators in classical logic.
IntervalBased Possibilistic Logic
 PROCEEDINGS OF THE TWENTYSECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Possibilistic logic is a wellknown framework for dealing with uncertainty and reasoning under inconsistent knowledge bases. Standard possibilistic logic expressions are propositional logic formulas associated with positive real degrees belonging to [0,1]. However, in practice it may be difficult fo ..."
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Cited by 1 (1 self)
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Possibilistic logic is a wellknown framework for dealing with uncertainty and reasoning under inconsistent knowledge bases. Standard possibilistic logic expressions are propositional logic formulas associated with positive real degrees belonging to [0,1]. However, in practice it may be difficult for an expert to provide exact degrees associated with formulas of a knowledge base. This paper proposes a flexible representation of uncertain information where the weights associated with formulas are in the form of intervals. We first study a framework for reasoning with intervalbased possibilistic knowledge bases by extending main concepts of possibilistic logic such as the ones of necessity and possibility measures. We then provide a characterization of an intervalbased possibilistic logic base by means of a concept of compatible standard possibilistic logic bases. We show that intervalbased possibilistic logic extends possibilistic logic in the case where all intervals are singletons. Lastly, we provide computational complexity results of deriving plausible conclusions from intervalbased possibilistic bases and we show that the flexibility in representing uncertain information is handled without extra computational costs.
On the Compilation of Stratified Belief Bases under Linear and Possibilistic Logic Policies
"... Developing efficient approaches for reasoning under inconsistency is an important issue in many applications. Several methods have been proposed to compile, possibly inconsistent, weighted or stratified bases. This paper focuses on the wellknown linear order and possibilistic logic strategies. It p ..."
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Developing efficient approaches for reasoning under inconsistency is an important issue in many applications. Several methods have been proposed to compile, possibly inconsistent, weighted or stratified bases. This paper focuses on the wellknown linear order and possibilistic logic strategies. It provides a way for compiling a stratified belief base in order to be able to process inference from it in polynomial time. The resulting extra compilation cost is very low. In particular, the number of additional variables, that are added to original stratified bases, corresponds exactly to the number of priority levels existing in the base. Moreover, our compilation approach allows an efficient computation of weighted possibilistic conclusions and possibilistic conditioning. 1
Approximation of Conditional Preferences Networks “CPnets ” in Possibilistic Logic
"... Abstract — This paper proposes a first comparative study of the expressive power of two approaches to the representation of preferences: the conditional preferences networks (CPnets) and a logical preference representation framework, namely the possibilistic logic. It is shown that possibilistic lo ..."
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Abstract — This paper proposes a first comparative study of the expressive power of two approaches to the representation of preferences: the conditional preferences networks (CPnets) and a logical preference representation framework, namely the possibilistic logic. It is shown that possibilistic logic, using a method for handling symbolic priority weights, can always provide complete preorders compatible with the partial CPnet order. Although CPnets provide an intuitive appealing setting for expressing preferences, possibilistic logic appears to be somewhat more flexible for that purpose. I.
Extending Uncertainty Formalisms to Linear Constraints and Other Complex Formalisms
, 2007
"... Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set ..."
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Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this difficulty, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a latticevalued possibilistic logic, an assumptionbased reasoning formalism and a DempsterShafer representation, proving some fundamental results for these extended systems. Our results on extending uncertainty formalisms also apply to a very general class of underlying monotonic logics.