Dana Nau and artificial intelligence
Dana Nau, a researcher in the field of game theory and automatic planning, is known for identifying “pathological” games in which, counter-intuitively, looking ahead leads to poorer decisions.
Dana Nau(born 1951) is a professor in the Computer Science Department and Systems Research Institute (ISR) at the University of Maryland. Nau’s work on automatic programming and game theory led him to the discovery of such “pathological” games, and he has made significant contributions to the theory and its application to automatic programming. He and his students have won numerous awards for algorithms developed for AI planning, manufacturing planning, zero-sum games, and non-zero-sum games.
His SHOP and SHOP2 planning systems have been downloaded more than 13, 000 times and used in thousands of projects worldwide. Dana has published over 300 papers, several of which have won best Paper awards, and is co-author of Automated Planning: Theory and Practice. He is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI). In addition to his professorship at the University of Maryland, Dana has held related positions in the Institute for Advanced Computing (UMIACS) and the Department of Mechanical Engineering, as well as co-directing the Computational Cultural Dynamics Laboratory (LCCD).
Richard Korf and artificial Intelligence
Richard Korf, who studies problem solving, heuristic search and planning in artificial intelligence, discovered depth-first search with iterative deepening — an approach similar to progressive deepening.
Richard Korf(born 1955) is a professor of Computer Science at the University of California, Los Angeles. He received his B.S. from MIT in 1977 and his M.S. and Ph.D. degrees in computer science from Carnegie Mellon University in 1980 and 1983, respectively. From 1983 to 1985 he was the Herbert M. Singer Assistant Professor in the School of Computer Science at Columbia University. His research areas are problem solving, heuristic search, and artificial intelligence planning.
Of particular note, in 1985, he discovered the iterative deepening method, which improves the efficiency of depth-first search. He also discovered the famous best solution to the Rubik’s cube in 1997. He is the author of Learning to Solve Problems by Searching for Macro-Operators (Pitman, 1985). He is a member of the editorial boards of the magazines Artificial Intelligence and Applied Intelligence. Dr. Korf was the recipient of the IBM Faculty Development Award in 1985 and NSF’s Residential Young Investigator in 1986 The UCLA Computer Science Department Distinguished Teaching Award (1989) and the Lockheed Martin Award for Excellence in Teaching (2005) Martin Excellence in Teaching Award). He is a Senior Fellow of the American Association for Artificial Intelligence.