Because uncertain reasoning is often intractable, it is hard to reason with a large amount of knowledge. One solution to this problem is to specify a set of possible models, some simple and some complex, and choose which to use based on the problem. We present an architecture for interpreting temporal data, called AIID, that incrementally constructs belief networks based on data that arrives asynchronously. It synthesizes the opportunistic control of the blackboard architecture with recent work on constructing belief networks from fragments. We have implemented this architecture in the domain of military analysis.
|Title of host publication||Advances in Intelligent Data Analysis V|
|Subtitle of host publication||5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings|
|Editors||Michael R. Berthold, Hans-Joachim Lenz, Elizabeth Bradley, Rudolf Kruse, Christian Borgelt|
|Number of pages||11|
|Publication status||Published - 2003|
|Name||Lecture Notes in Computer Science|
|Publisher||Springer Berlin Heidelberg|