On the Automatic Generation of Case Libraries
by Chunking Chess Games

Stephen Flinter and Mark T. Keane

Trinity College Dublin


Abstract. As a research topic computer game playing has contributed
problems to AI that manifest exponential growth in the problem space.
For the most part, in games such as chess and checkers these problems
have been surmounted with enormous computing power on brute-force
search methods using massive databases. It remains to be seen whether
such techniques will extend to other games such as go and shogi. One
suggestion is that these games and even chess might benefit from a
knowledge-based treatment but such approaches have met with limited
success. The problem, as ever from such approaches, is the characterisation 
of the knowledge to be used by the system. This paper deals with
the TAL system, which employs case-based reasoning techniques for chess
playing. In the paper, rather than focus on playing, we concentrate on
the automatic generation of suitable case knowledge using a chunking
technique on a corpus of grandmaster games.
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