The Significance of Kasparov versus D EEP B LUE and the Future of Computer Chess

Abstract

In this paper we argue that the Garry Kasparov versus DEEP BLUE matches of 1996 and 1997, despite some shortcomings, have had a significant positive impact on computer-chess and artificial-intelligence research. Such matches can be viewed as valuable lessons in how (and how not) to use man-machine competitions to obtain a better focus on applied research and on the development efforts. One of the most important goals is to provide benchmarks (both in terms of development effort and match performance) that more knowledge-intensive (i.e., less brute-force) approaches must surpass to justify their extra complexity.

We will briefly summarize some of the latest developments in computer-chess research and highlight how our work on a program called CHESTER tries to build on them to provide such justifications. Using our CHESTER work as an example, we propose a possible shift in emphasis for the computer-chess community: away from tournament performances (nowadays a short-term goal) and more toward treating the chess domain as a valuable testbed for research on emerging large-scale data-mining and machine-learning techniques. The successes of DEEP BLUE, despite legitimate questions of validity and scope, have in some sense freed (and perhaps even compelled) us to refocus on such longer-term goals.