The automated prediction of a user’s interests and requirements is an area of interest to the Artificial Intelligence community. However, current predictive statistical approaches are subject to theoretical and practical limitations which restrict their ability to make useful predictions in domains such as the WWW and computer games that have vast numbers of values for variables of interest. In this paper, we describe an automated abstraction technique which addresses this problem in the context of Dynamic Bayesian Networks. We compare the performance and computational requirements of fine-grained models built with precise variable values with the performance and requirements of a coarse-grained model built with abstracted values. Our results indicate that complex, coarse-grained models offer performance and computational advantages compared to simpler, fine-grained models.
|Title of host publication||PRICAI 2000 Topics in Artificial Intelligence|
|Subtitle of host publication||6th Pacific Rim International Conference on Artificial Intelligence Melbourne, Australia, August 28 - September 1,2000 Proceedings |
|Editors||Riichiro Mizoguchi, John Slaney|
|Place of Publication||Berlin Germany|
|Number of pages||11|
|Publication status||Published - 2000|
|Name||Lecture Notes in Artificial Intelligence|