Collective Inference with Learned and Engineered Knowledge
Final rept. 12 Feb 2004-31 May 2006
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
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A persistent goal of research in artificial intelligence has been to enable learning and reasoning with probabilistic models in complex domains. Much of this work has been directed toward systems that complement, rather than replace, human abilities and knowledge. Models that fuse engineering knowledge knowledge from human sources with learned information information gained algorithmically can take advantage of the strengths of both approaches, yielding more accurate predictions. A particularly fruitful area for this research is improving our understanding of emergent behavior, specifically, how connectivity among individual units of a system affects global behavior. The Knowledge Discovery Laboratory KDL seeks to apply a growing understanding of emergent behavior to the design of learning and reasoning systems.
- Statistics and Probability