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Re: [computer-go] Learning : was Chess programs versus go programs
Vincent, I don't necessarily disagree with you (about search). As you recall in my message, I summarized my point like this:
There is little question in my mind that searching is a key to computer-go
just as it was in chess. It may not be the same kind of search (muti-level
for example), and the evaluations (including life and death) may not be as
easy but I think its the most promising direction to pursue in combination
with pattern based concepts for move generation and help in evaluation.
So, the search technique is primary, of course paired with other things including solid evaluation. Pattern or rule based module components could be hand coded, or auto generated/learned, or even coded by a 1000 monkeys as long as they work reasonably well. But I've done enough hand tuning to know that intuition after a few iterations is as often wrong as it is right. There is a lot of trial and error and guessing, albeit educated guessing. Each method has its pros and cons. I'm well aware that you don't see value in automated learning or pattern harvesting. And I certainly don't expect any learning system to "beat" human learning capacity. That's not the goal or the expectation, however. There is something appealing about the objective and statistical nature of the pattern harvesting technique that Frank has developed over the ad-hoc and "expert" tuned pattern/rule sets commonly used. I would not discount it as a useful part of a go-playing program. As Frank has said it's designed to be a better Joseki/Fuseki database, not the holy grail. There is nothing preventing combining it with other modules using other techniques for handling some tactical issues, local search, ladders, ko fights, life/death analysis, etc.
You seem to have a tendency to try to frame things as black and white only, then attack the extreme case, when no-one is really talking about the extreme case.
Regards,
Matt
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