Mallory Selfridge, Donald J. Dickerson, and Stanley F. Biggs

Research at the Artificial Intelligence Laboratory of the University of Connecticut is currently focused on a number of projects addressing both fundamental and applied aspects of next-generation expert systems and machine learning. We believe that these next-generation expert systems will have to be based on cognitive models of expert human reasoning and learning in order to perform with the ability of a human expert. Cognitive expert systems should display three characteristics. First, because expert human reasoning and learning rely in part on qualitative causal models and large-scale event-based memory structures, cognitive expert systems should rely on similar knowledge. Second, because human experts are skilled at acquiring knowledge, often through natural language interaction, cognitive expert systems should learn through real-world natural language interaction.