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Adaptive Merging of Prioritized Knowledge Bases

Liu W., Qi G., Bell D.A.

Fundamenta Informaticae

2006

Vol. 73, nr 3

389-407

In this paper, we propose an adaptive algorithm for merging n (n ł 2) prioritized knowledge bases which takes into account the degrees of conflict and agreement among these knowledge bases. The algorithm first selects largely partially maximal consistent subsets (LPMCS) of sources by assessing how (partially) consistent the information in the subset is. Then within each of these created subsets, a maximal consistent subset is further selected and knowledge bases in it are merged with a suitable conjunctive operator based on the degree of agreement among them. This result is then merged with the remaining knowledge bases in the corresponding LPMCS in the second step through the relaxation of the minimum operator. Finally, the knowledge bases obtained from the second step are merged by a maximum operator. In comparison with other merging methods, our approach is more context dependent and is especially useful when most sources of information are in conflict.

Quasi-Possibilistic Logic and its Measures of Information and Conflict

Dubois D., Konieczny S., Prade H.

Fundamenta Informaticae

2003

Vol. 57, nr 2-4

101--125

Possibilistic logic and quasi-classical logic are two logics that were developed in artificial intelligence for coping with inconsistency in different ways, yet preserving the main features of classical logic. This paper presents a new logic, called quasi-possibilistic logic, that encompasses possibilistic logic and quasi-classical logic, and preserves the merits of both logics. Indeed, it can handle plain conflicts taking place at the same level of certainty (as in quasi-classical logic), and take advantage of the stratification of the knowledge base into certainty layers for introducing gradedness in conflict analysis (as in possibilistic logic). When querying knowledge bases, it may be of interest to evaluate the extent to which the relevant available information is precise and consistent. The paper review measures of (im)precision and inconsistency/conflict existing in possibilistic logic and quasi-classical logic, and proposes generalized measures in the unified framework.

Using possibilistic logic for modeling qualitative decision: ATMS-based algorithms

Dubois D., Le Berre D., Prade H., Sabbadin R.

Fundamenta Informaticae

1999

Vol. 37, Nr 1,2

1-30

This paper describes a logical machinery for computing decision, where the available knowledge on the state of the world is described by a possibilistic prepositional logic base (i.e., a collection of logical statements associated with qualitative certainty levels), and where the preferences of the user are also described by another possibilistic logic base whose formula weights are interpreted in terms of priorities. Two attitudes are allowed for the decision maker: a pessimistic risk-averse one and an optimistic one. The computed decisions are in agreement with a qualitative counterpart to the classical theory of expected utility, recently developed by three of the authors. A link is established between this logical view of qualitative decision making and an ATMS-based computation procedure. Efficient algorithms for computing pessimistic and optimistic optimal decisions are finally given in this logical setting (using some previous work of the fourth author).