Compute-Intensive Methods for Artificial Intelligence

In the 70’s and 80’s the success of knowledge-intensive approaches to problem solving eclipsed earlier work on compute-intensive weak methods. However, in recent years, compute-intensive methods have made a surprising comeback. One of the most prominent examples is the success of IBM’s Deep Blue in defeating Gary Kasparov in the 1997 ACM Challenge match. Deep Blue’s performance led Kasparov to exclaim, “I could feel — I could smell — a new kind of intelligence across the table.” Deep Blue derives its strength mainly from highly optimized search (Kasparov 1997, McDermott 1997). Another dramatic development in the compute-intensive approach was the recent computer proof resolving the Robbins problem (Kolata 1996). The Robbins problem is a well-known problem in Boolean algebra, and was open for over sixty years. The computer proof was found by applying powerful search techniques guided by general search tactics. Several aspects of the computer proof could be called “creative” by mathematicians’ standards. Deep Blue’s performance and the resolution of Robbin’s theorem are good examples of a qualitative change in performance of compute-intensive approaches compared to just a few years ago.