Obstacles To High-level Performance
For a long time it was a widely held opinion that computer Go posed a problem fundamentally different to computer chess insofar as it was believed that methods relying on fast global search compared to human experts combined to relatively little domain knowledge would not be effective for Go. Therefore, a large part of the computer Go development effort was during these times focused on ways of representing human-like expert knowledge and combining this with local search to answer questions of a tactical nature. The result of this were programs that handled many situations well but which had very pronounced weaknesses compared to their overall handling of the game. Also, these classical programs gained almost nothing from increases in available computing power per se and progress in the field was generally slow.
A few researchers grasped the potential of probabilistic methods and predicted that they would come to dominate computer game-playing, but many others considered a strong Go-playing program something that could be achieved only in the far future, as a result of fundamental advances in general artificial intelligence technology. Even writing a program capable of automatically determining the winner of a finished game was seen as no trivial matter.
The advent of programs based on Monte Carlo search starting in 2006 changed this situation in many ways, although the gap between professional human players and the strongest Go programs remains considerable.
Read more about this topic: Computer Go
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