In this talk, I address the challenges of information/knowledge fusion from multiple (possibly conflicting) sources. For example, consider that there are multiple experts (sources) providing knowledge-based models of the same scenario/situation and we wish to aggregate this information in order to assist in decision-making. There are several problems we may run into by naively merging the information from each source - the experts may disagree on the probability (uncertainty) of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A); the experts may even disagree on the entire structure of dependencies among a set of variables in a (probabilistic) network. The challenge here is to develop a semantically sound and computationally effective methodology that explicitly accounts for the uncertainty and conflicts. In our solution to this problem, we represent the knowledge-based models as Bayesian Knowledge Bases (BKBs) and provide an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert/source decision making by providing a framework in which all opinions can be preserved and reasoned over. [Go to the full record in the library's catalogue]
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