Thorsten, I basically agree with what you are saying, it is the definitions that I have problems with.mclane wrote: ↑Sun Mar 22, 2020 8:26 pmLook ahead with millions of NPS is not planning for me.
It is also not knowledge for me.
A plan needs no search depth of 30-40.
It needs no AB search at all.
A plan is a concept.
Where to manoeuvre the pieces and WHY to do so.
A plan is a thesis.
And the search tree could find out if the thesis Works.
But it can be build without proof that it works or not.
Chess engines have plenty of evaluation categories and give malus and bonus and sum up them into a value.
But this is not a plan.
This is knowledge and the knowledge can be put into consideration of the position.
A plan can be a mate attack or misusing a positional weakness. It can be important to bring a piece into a certain position or to build a certain pawn structure or destroy it.
A plan is NOT the main line the AB engine has found out.
Of course this CAN be a method to do it and it is a method that is used in most AB programs.
I do believe that programs like LC0 can play a better strategical chess even if they use very few resources then the usual AB engines.
This is because normal AB programs rely mainly on many NPS and huge search depth. Something that is IMO not important for the thing I am talking about.
Normal AB chess Engines misuse resources in a very Heavy way.
It’s been done because these resources are there and we have plenty of them. But they lead to a loss of planning because the programmers rely on search instead on creating a plan. The search replaces the plan and substitutes it.
IMO it would make more sense to stop this waste of resources and substitute search tree with planning.
Neural network engines are better strategists ( not planners ) than alpha-beta engines and are the kind of engines you are looking for from what I understood of your post. The big nets searching only hundred of nodes per move are already very strong, so this is not different from how humans play. Also, the actual planning method ( MCTS ) is very selective and only considers the most viable moves, again just like humans.
That is why MCTS is considered a better planning method than "brute-force" alpha-beta in AI.
That GPUs can accelerate NN and give it more lookahead searching capability does not invalidate the fact that NN engines are more closer
to how humans think. Do you see anything missing from how NN engines play chess that you don't like?
I will try to recite the definitions of tactics, strategy, and planning as applied to chess
So that is why I was opposed to your claim that there is no chess engine that plans, because with the above definition all engines do planning unless they play with just static evaluation. I think what you are looking for is an engine that is a good strategist and does not rely heavily on the planning method ( i.e. lookahead search ) to achieve its goal, like alpha-beta engines mostly are.Strategy - is the long term goal (winning) and how you plan to achieve it. Control of center squares, pawn structure, mobility, etc fall in this category. You can think of a strategy for your opponent by studying his weaknesses and strengths even before play starts.
Tactics - is a short term goal achieved with very specific steps. e.g. Forking rook and king with a knight for an immediate benefit
One tactic is not going to get you to your goal, so you need many of those along the way.
Planning - is the method you use to achieve your goal ( i.e. realize your strategy ). The best strategy without good planning will fail.
In chess, you plan with alpha-beta, MCTS, and other lookahead search methods
Plan - is a sequence of strategic and tactical moves that will get you to the winning position.
Planning seeks to find a plan to achieve the desired outcome.