Conflict and surprise: heuristics for model revision
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Refinement and coarsening of Bayesian networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
State-space abstraction methods for approximate evaluation of bayesian networks
State-space abstraction methods for approximate evaluation of bayesian networks
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An agent based architecture that is modelled on a successfully operating process of the real world-criminal investigation-circumvents high computational costs caused by Bayesian fusion by realising a distributed local Bayesian fusion approach. The idea underlying local Bayesian fusion approaches is to perform Bayesian fusion at least not in detail on the whole space that is spanned by the Properties-of-Interest. Local Bayesian fusion is mainly based on coarsening and restriction techniques. Here, we focus on coarsening. We give an overview over the agent based conception and translate the proposed ideas in a formal mathematical framework.