golem7 wrote:I just want to add that the advance of computer go (as well as computer chess before) can mostly be attributed to new algorithmic approaches to the problem, that is computer science. Formal go research had nothing to do with it.
Go programs succeed because of a combination of a) new applied computer science applicable to different things and games, b) go research (such as formulating the goal to distinguish winning from losing final scoring positions, c) a few go-specific knowledge tricks (such as using a few selected patterns) and d) brute force applied to (a) to (c).
Actually this can even serve as a counterexample to Robert since the old traditional (weak) programs followed coded rules of play derived from human reasoning about go (aka principles).
No. It does not serve as a counter-example. Instead, the situation is a bit complicated.
Current go programs also use go knowledge, and research was needed to find which is useful for MC programs. Surprisingly, very little go knowledge is needed for them. Research was needed to come to this conclusion, although little "formal" research. Some call it "engeneering".
Earlier weak programs tried various methods, and often combinations of them. E.g., combinations of heuristics and pattern (example) databases. Therefore, one cannot easily conclude that, in the past, usage of principles was improper; it could as well have been usage of example databases that hurt the usage of principles.
Expert system go programs of the past used, IMO, hopelessly weak or insufficient heuristics, and without a clear system and good hierarchy. Therefore, the past experience with weak expert systems is not a counter-example for designing expert system go programs at all. Rather it proves that badly chosen / codified expert knowlegde leads to relatively weak results. In the future, expert system can become much stronger than today, but currently programmers have no incentive to follow this path, ALA monte-carlo is a low effort path with (judging winning rates only) sufficiently good results.
Expert system go programs are no counter-example to human go knowledge use, because humans do not work like (Turing) computers.
Modern programs using Monte-Carlo-evaluation find strong moves by running lots of simulations, collecting statistics and selecting the best move based on that (examples).
No. The MC programs use a combination of (a) to (d). They do not use only (d).
Principles have to be taught by example, there is no way around that.
Completely wrong.
1) Principles can be taught explicitly, e.g., by text, and then illustrated by examples.
2) Alternatively (NOT: exclusively!), examples can be shown, and their consumer is expected to invent and derive suitably fitting principles on his own. This does not prevent learners from them stating the derived principles explicitly, so that then other learners have the choice for learning principles by (1) or (2).
The problem is to select (or create) such examples that important principles can be derived easily which is the task of the teacher/author.
This is a problem only for (2). For (1), the learner can profit also from, but need not, particularly well chosen examples.
And in the end there is an exception to every rule and principle, every game is completely different (that's why we love it!), even one stone somewhere on the board can change the local situation completely.
In particular, there are also principles without exception, regardless of every game being different and one stone affecting the local situation completely.
That is why the only real source of go strength is reading power,
This is completely wrong, because it is saying that brute force reading almost alone could generate much go strength. This is completely wrong, because the game tree explodes very quickly.
It is always a combination of - conscious or subconscious - go theory knowledge for how to prune reading well and correctly and the reading power itself.
has to be trained.
Reading power has to be trained so that the combination of go theory knowledge and reading power generates go strength.
There is no quick and easy way to strength.
In this generality, it is wrong. E.g., there are very quickly improving kyu players.
It is also very misleading, because there are ways to ease improvement and there are ways to greatly accelerate improvement. Except that such ways do not succeed for all players; so you can say "There is no quick and easy way to strength working (equally) well for all players.".