IRI-9529227 Kasif, Simon Johns Hopkins University $30,620 - 12 mos. SGER: Fast Queries and Updates in Probabilistic Networks Knowledge representation using networks is one of the most promising approaches to Artificial Intelligence. The main goal of another current grant to the same investigator is to develop very efficient algorithms for network-based AI tools such as constraint networks and probabilistic networks, focusing on utilizing parallel processing to achieve execution time that is sub-linear in the size of the network. A by-product of this existing project recently was several new compilation strategies for probabilistic networks that allow, after fast preprocessing, the processing of both queries and updates in time that is logarithmic in the size of the database. This represents an exponential improvement in current algorithms. At present, the techniques are known to be applicable to certain special cases, but it appears possible to extend them to more general networks. If they can be extended adequately, it will have a strong impact on technology, given the wide applicability of the ability to define, manipulate, and update probabilistic information. This small grant for exploratory research is to explore the applicability of these techniques, which were not a part of the existing grant's research plan.
