Programming Cognitive Robots
cs.toronto.edu
cs.toronto.edu
But it seems like... cognitive AI hasn't paid off? Big & deep Neural Nets are the type of ML/AI that are achieving milestones in learning, gameplay and tasks.
If someone has a strong case to make for cognitive programming, I'd love to hear it. But right now it seems like it's a heuristics-based system that's destined to lose to NNs.
(And before the nitpickers arrive, I totally grant that using heuristics for toy robots makes sense, and is a good way to expose people to programming.)
AlphaGo/MuZero are completely NN based and were so far ahead of the competition when they were developed they led to the whole wave of NN-for-eval that we seen now. And AlphaGo/MuZero doesn't compete in TCEC.
The chess community (especially the stockfish programming group) is very focused on improving their own system. I don't think the fact that is a the strongest system really means much - it's pretty clear they are leaving performance on the table. For example it wasn't until last year (!) that they moved to a GPU based training system.
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.
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.
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.
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.
it could depend on downstream task, is it differentiable or not. Ray tracing of convex textures is likely differentiable, win/loss function for a given chess move maybe not.
By making something differentiable, you're hoping relaxations or some smooth proxy doesn't break fundamental structure, allowing you to solve an easier problem than discrete combinatorial search. Sometimes there can be barely any leveragable structure. This has proved very difficult to achieve in general and is a sort of holy grail in some fields.
That said, there are powerful game playing AIs that use either no (DeepNash) or minimal (DORA) search. But in general you can't get something for free, you're always paying something with an approximation.
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.
I think the distinction is how the looked-up knowledge is integrated.
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.
The rest is basically regular programming in that its largely bound by how well the programmer models. For a large number of things, the scope of what a human can consider lags an optimization process. In chess, it at first seemed like this might be the case there too, but latest performance points to maybe not this time.
I've actually done a blocks world on a real robot (an IBM RS-1, of all things) using that approach, programming in LISP. Yes, it worked. No, it can't deal with an unstructured or dynamic world. Predicate calculus is fine for program proving, but terrible for real world robots.