Gunnar Farnebäck wrote:
Don wrote:
Of course now we just had to go and spoil it all by imposing domain
specific rules. I have done the same and I admit it. It would be fun
to see how far we could go if domain specific knowledge was forbidden as
an experiment. Once patterns are introduced along with other direct Go
knowledge, it's still fun but it feels a bit "wrong", kind of like
cheating. It's clear that when we do this, we introduce strengths and
weaknesses to the program, making it a bit more fragile, less
"universal" or robust. Stronger too, but more susceptible to
in-transitivity.
I'm on the other side of this issue. In my opinion all kinds of go
knowledge are fair game and I'm rather disappointed that so small
amounts of domain specific knowledge have been merged with the UCT
search approaches.
Thank you for articulating this viewpoint, Gunnar.
A program trained upon millions of patterns harvested from professional games
[appearing soon on a server near you, for some appropriate value of "soon"]
has the property that it _severely_ prunes the game tree according to what
might be described as "heuristics", but which heuristics are _learned_ by
several hundred feedforward networks. [The heuristics are not hand-coded.]
The patterns comprise observations of what pros do, and more importantly,
what they don't do. Selecting which features of go-behavior to measure,
and upon what scale to express those measurements, are both, of course,
crucial. [The feature-extraction phase of pattern-recognition is a tricky
problem, regardless of domain: Eschewing noise in favor of information.]
Once the feedforward networks have done their learning, the patterns themselves
are discarded. [Thus there is no pattern "library", only the trained networks.]
Applying a UCT-layer atop such a severely-pruned game-tree seems promising.
I would re-phrase what Gunnar said: I am optimistic that when UCT is
layered atop a strong program that has already pruned -- via learned,
domain-specific knowledge -- most of the fruitless branches from the
tree, such a program will become even stronger by virtue of the UCT-layer.
Don is right, too, in the sense that such a program is somehow less "pure"
than one which has obtained all its knowledge from first principles.
Nonetheless, a program that could not only play a decent game of go, but
somehow emulate the _style_ of a given professional would be of interest,
would it not?
Of even more interest, perhaps, would be a program that, given a game of
go between two (unidentified) professionals whose games it had studied,
whose styles it had _learned_, could then tell you who the two players
were, _even_if_it_had_never_before_seen_that_particular_game_.
Alternatively, if the identity of the players were known, such a program
could be expected to perform well (much better than random, anyway) at a
"guess-the-next-move" task.
Could a pure-UCT program ever perform in these ways? Even if the pure-UCT
program were able to reliably and repeatedly defeat the domain-specific
program at playing go, it would not be able to perform the other tasks.
So, I must agree with Gunnar that the domain-specific approach is "fair game".
It all depends on which problem one is trying to solve, to begin with.
--
Rich
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