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AlphaGo/MuZero/LeelaZero are not completely NN-based. They're still Monte Carlo Tree Search. Completely NN would have to be some sort of bitboards in moves out

AlphaGo/MuZero/LeelaZero are not completely NN-based. They're still Monte Carlo Tree Search. Completely NN would have to be some sort of bitboards in moves out thing. That's not a thing afaik. They rely a lot more on the NN though is my understanding, sacrificing search depth for the compute. LeelaZero does compete, and Stockfish tends to outcompete it. It's my feeling that MCTS, while necessary with go's branching factor making minimax approaches untenable, is not actually a win in chess compared to alphabeta paired with the decades of heuristics that have been developed. I have some complex reasons for believing this. One of them is that Stockfish' move ordering relies heavily on statistical "learning"(histories, continuations, killer move heuristic, etc) heuristics that are probably more powerful if your search is wider. This probably applies to Transposition/pawn tables as well. More importantly, I'm sceptical of the amount of useful inference that can be made from a static position in chess vs go. I barely even know the rules of go, but I am an expert level chess player. I think go has a lot more static structure that likely makes larger NNs more useful than they are in chess. Chess is far more illuminated by specific tactical themes in the position that are very effectively picked out through qsearch paired with move ordering heuristics and caching. With chess, I think there's some point when inferring anything more is just gonna involve search anyway. And you don't want ad hoc search appearing in the eval function because that won't benefit from tables and move ordering heuristics. And yes, certainly there's lots more to gain for Stockfish, and their incessant improvement is impressive. And obviously the size of their community is a huge advantage. But I don't actually think MCTS is the way to go for chess, ultimately. Minimax based techniques are just too damn effective, with a sprinkle of NN. Shogi is an interesting in between here. Conceptually like chess, but with a much higher branching factor. I'm not at all aware of the state of the art of shogi minimax search though.

evolvingstuff 4y ago
So I just googled "differentiable Monte Carlo" and it looks like that concept has so far only been applied to ray tracing, but I would be shocked if that (or similar) doesn't become a thing in the next year or three, such that AlphaGo et al become end-to-end differentiable.
- mtlmtlmtlmtl 4y ago
  There's more to Monte Carlo Tree Search than the Monte Carlo part though. It's a tree search algorithm using Monte Carlo methods to direct the search, so it would still not be a complete NN engine. Also, there's no reason to expect a "naked" neural net to ever hold up against current chess engines. Engines are already far beyond the strongest humans, who can be seen as naked NN players. Except the NN is far beyond anything we could even dream of building today, both in number of parameters and the complexity of a single neuron, not to mention our ability to learn. NNs are interesting in areas where human performance is not yet attained. Chess is not one of those. Heuristic algorithms are far stronger than humans, and so it seems strange to me to suggest making engines stronger by making them more and more like humans, just with far less compute, and weaker learning methods. The notion seems inherently ill-conceived to me.
  - riku_iki 4y ago
    > stronger by making them more and more like humans, just with far less compute, and weaker learning methods. The notion seems inherently ill-conceived to me. human brain while may have larger raw compute power comparing to TPU pod has also many bottlenecks, brain works on much slower frequency per current research than 2GHz TPUs, inputs are very bottle-necked, you can't feed brain with 1TB of text in one day like you can do with neural network.
    - mtlmtlmtlmtl 4y ago
      You're right of course. Mainly by compute I'm referring to number of and complexity of neurons, not frequency. You put your finger on something though because the bottlenecks of brains are probably the reason why traditional minimax searchers are so good at chess: working memory. Humans just don't have the working memory to search to the depths computers can. frequency is interesting, but chess is inherently "working memory-bound". If you gave Stockfish a normal time control and a grandmaster two days per move, Stockfish would still win. Past about 30 minutes of thought on a position, there's not much more progress a human can make. But if you also gave the GM a second board they were allowed to make moves on, and a notebook, I bet the GM would win, because at that point you're only comparing eval functions, and a GM would still be miles ahead. I wonder if someone's done this experiment.
nl 4y ago
Yes, you are right that MCTS (or some search at least) is an important factor in addition to NN based eval. If you mean this for "classical algorithms" then sure. But in the context of AI "classical algorithms" is more like logic programming (eg Prolog) or similar in the "cognitive programming" vein that led off this discussion. I don't think these types of classical algorithms are competitive.
- mtlmtlmtlmtl 4y ago
  By classical here I simply meant non-connectionist methods in general.
  - nl 4y ago
    I guess I've never thought of search as being either connectionist or non-connectionist. It's self-evident that people search (eg look up in books) things that aren't in local memory. I think the distinction is how the looked-up knowledge is integrated.
    - jens-c 4y ago
      The non-connectionist, classical, symbolical aspect is not so much "search" per se, but the fact that MCTS performs a form of look-ahead, which requires manually encoding the rules of the game in some fashion. Determining what the legal moves in a state are, and what state results from a given move, is not something the AI would learn by itself, but is programmed into the system by a human expert.
      - Vetch 4y ago
        I do not believe this is still true in MuZero, where no rules are ever explicitly encoded, beyond what leaks in from the reward function?
        jens-c 4y ago
        Indeed, in this latest iteration the network learns a model of the game by itself. Note however that it still uses MCTS and performs a look-ahead, i.e. it is still being "told" by the programmers how to do search (planning), only now it is left to the system to determine how it wants to represent states/actions/policies internally.

evolvingstuff 4y ago

So I just googled "differentiable Monte Carlo" and it looks like that concept has so far only been applied to ray tracing, but I would be shocked if that (or similar) doesn't become a thing in the next year or three, such that AlphaGo et al become end-to-end differentiable.

mtlmtlmtlmtl 4y ago
There's more to Monte Carlo Tree Search than the Monte Carlo part though. It's a tree search algorithm using Monte Carlo methods to direct the search, so it would still not be a complete NN engine. Also, there's no reason to expect a "naked" neural net to ever hold up against current chess engines. Engines are already far beyond the strongest humans, who can be seen as naked NN players. Except the NN is far beyond anything we could even dream of building today, both in number of parameters and the complexity of a single neuron, not to mention our ability to learn. NNs are interesting in areas where human performance is not yet attained. Chess is not one of those. Heuristic algorithms are far stronger than humans, and so it seems strange to me to suggest making engines stronger by making them more and more like humans, just with far less compute, and weaker learning methods. The notion seems inherently ill-conceived to me.
- riku_iki 4y ago
  > stronger by making them more and more like humans, just with far less compute, and weaker learning methods. The notion seems inherently ill-conceived to me. human brain while may have larger raw compute power comparing to TPU pod has also many bottlenecks, brain works on much slower frequency per current research than 2GHz TPUs, inputs are very bottle-necked, you can't feed brain with 1TB of text in one day like you can do with neural network.
  - mtlmtlmtlmtl 4y ago
    You're right of course. Mainly by compute I'm referring to number of and complexity of neurons, not frequency. You put your finger on something though because the bottlenecks of brains are probably the reason why traditional minimax searchers are so good at chess: working memory. Humans just don't have the working memory to search to the depths computers can. frequency is interesting, but chess is inherently "working memory-bound". If you gave Stockfish a normal time control and a grandmaster two days per move, Stockfish would still win. Past about 30 minutes of thought on a position, there's not much more progress a human can make. But if you also gave the GM a second board they were allowed to make moves on, and a notebook, I bet the GM would win, because at that point you're only comparing eval functions, and a GM would still be miles ahead. I wonder if someone's done this experiment.

nl 4y ago

Yes, you are right that MCTS (or some search at least) is an important factor in addition to NN based eval. If you mean this for "classical algorithms" then sure. But in the context of AI "classical algorithms" is more like logic programming (eg Prolog) or similar in the "cognitive programming" vein that led off this discussion. I don't think these types of classical algorithms are competitive.

mtlmtlmtlmtl 4y ago
By classical here I simply meant non-connectionist methods in general.
- nl 4y ago
  I guess I've never thought of search as being either connectionist or non-connectionist. It's self-evident that people search (eg look up in books) things that aren't in local memory. I think the distinction is how the looked-up knowledge is integrated.
  - jens-c 4y ago
    The non-connectionist, classical, symbolical aspect is not so much "search" per se, but the fact that MCTS performs a form of look-ahead, which requires manually encoding the rules of the game in some fashion. Determining what the legal moves in a state are, and what state results from a given move, is not something the AI would learn by itself, but is programmed into the system by a human expert.
    - Vetch 4y ago
      I do not believe this is still true in MuZero, where no rules are ever explicitly encoded, beyond what leaks in from the reward function?
      - jens-c 4y ago
        Indeed, in this latest iteration the network learns a model of the game by itself. Note however that it still uses MCTS and performs a look-ahead, i.e. it is still being "told" by the programmers how to do search (planning), only now it is left to the system to determine how it wants to represent states/actions/policies internally.