Handbook of logic in artificial intelligence and logic programming (vol. 3)
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
Arbitration (or How to Merge Knowledge Bases)
IEEE Transactions on Knowledge and Data Engineering
Quasi-possibilistic logic and its measures of information and conflict
Fundamenta Informaticae
Weakening conflicting information for iterated revision and knowledge integration
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Artificial Intelligence - Special issue on nonmonotonic reasoning
A negotiation-style framework for non-prioritised revision
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
A split-combination approach to merging knowledge bases in possibilistic logic
Annals of Mathematics and Artificial Intelligence
Combining multiple knowledge bases by negotiation: a possibilistic approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
On the Definition of Essential and Contingent Properties of Subjective Belief Bases
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A novel Bayesian learning method for information aggregation in modular neural networks
Expert Systems with Applications: An International Journal
Knowledge and Information Systems
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Engineering Applications of Artificial Intelligence
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Recently, several belief negotiation models have been introduced to deal with the problem of belief merging. A negotiation model usually consists of two functions: a negotiation function and a weakening function. A negotiation function is defined to choose the weakest sources and these sources will weaken their point of view using a weakening function. However, the currently available belief negotiation models are based on classical logic, which makes them difficult to define weakening functions. In this paper, we define a prioritized belief negotiation model in the framework of possibilistic logic. The priority between formulae provides us with important information to decide which beliefs should be discarded. The problem of merging uncertain information from different sources is then solved by two steps. First, beliefs in the original knowledge bases will be weakened to resolve inconsistencies among them. This step is based on a prioritized belief negotiation model. Second, the knowledge bases obtained by the first step are combined using a conjunctive operator which may have a reinforcement effect in possibilistic logic.