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TOPIC: Artificial intelligenceClear

Kenneth De Jong

Professor of Computer Science

Expertise: Genetic algorithms, Evolutionary computation, Machine learning, Artificial intelligence, Complex adaptive systems

De Jong came to Mason in 1984. He is head of the Evolutionary Computation Laboratory and associate director of the Krasnow Institute. His research interests include genetic algorithms, evolutionary computation, machine learning, and adaptive systems. He also is interested in experience-based learning in which systems must improve their performance while actually performing the desired tasks in environments not directly in their control or the control of a benevolent teacher. Support for these projects is provided by the Defense Advanced Research Projects Agency, the Office of Naval Research, and the Naval Research Laboratory. A member of the evolutionary computation research community, De Jong has been involved in organizing many of the workshops and conferences in this area. He is the founding editor in chief of the journal Evolutionary Computation and a member of the board of the Association for Computing Machinery Special Interest Group for Genetic and Evolutionary Computation.

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Gheorghe Tecuci

Professor of Computer Science; Director of the Learning Agents Center

Expertise: Artificial intelligence, Machine learning, Knowledge engineering, Knowledge acquisition and problem solving

Tecuci's research is focused on creating a theory for the development of knowledge-based agents by typical users who do not have knowledge engineering experience. The envisioned theory will allow these typical users to develop intelligent assistants that incorporate their problem solving expertise, and will thus contribute to a new revolution in the use of computers (where typical users will no longer be just users of programs developed by others, but agent developers themselves).

As part of this long-term research effort,  Tecuci has originated or contributed to several important concepts in intelligent agents, machine learning and knowledge acquisition, including: multistrategy learning, learning agent shell, plausible explanations, plausible version spaces, plausible justification trees, understanding-based knowledge extension, consistency-driven knowledge elicitation, integrated teaching and learning, and mixed-initiative reasoning. These contributions have led to the “Disciple” agent development approach where a subject matter expert teaches a Disciple learning agent to become a knowledge-based assistant, in a way that is similar to how the expert would teach a human apprentice, through specific problem solving examples and explanations, and by supervising and correcting agent’s problem solving behavior.

 

Media Contact: Preston Williams, 703-993-9376, pwilli20@gmu.edu