Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut
AbstractIn order for next-generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning. We call such next-generation systems cognitive expert systems. Research at the Artificial Intelligence Laboratory at the University of Connecticut is directed toward understanding the principles underlying cognitive expert systems and developing computer programs embodying those principles. The Causal Model Acquisition System (CMACS) learns causal models of physical mechanisms by understanding real-world natural language explanations of those mechanisms. The going Concern Expert ( GCX) uses business and environmental knowledge to assess whether a company will remain in business for at least the following year. The Business Information System (BIS) acquires business and environmental knowledge from in-depth reading of real-world news stories. These systems are based on theories of expert human reasoning and learning, and thus represent steps toward next-generation cognitive expert systems.
How to Cite
Selfridge, M., Dickerson, D. J., & Biggs, S. F. (1987). Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut. AI Magazine, 8(1), 75. https://doi.org/10.1609/aimag.v8i1.577
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