Brief Description

The problem of merging multiple sources of information is an important issue in many AI fields, such as multiple-agent systems, intelligent information fusion from multiple sources and expert opinions pooling etc. Information in each source is often expressed as a set of logical formulae and may be pervaded with uncertainty. In addition, inconsistencies may exist among different sources. Therefore, a powerful automated reasoning mechanism is needed to model and reason with uncertainty within a source and to deal with inconsistencies among these sources when merging information.

Possibilistic logic provides a good framework to cope with both of these issues. The objectives of this project include:

1. Assess current merging operators and propose alternative merging approaches.

2. Assess the complexity of different merging operators and their constraints.

3. Apply the proposed merging methods to information fusion from multiple sources, multi-agent systems, and ontology management on the Web.

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