"Aurelian Florea wrote on 2017-12-15 EST

@Vickalan

I will make sure that machine learning does invade the chess variants world. :)!"

Good! Make me an opponent for Macysburg and its bigger (and smaller) relatives. ;) I need a good opponent to learn from.

"V. Reinhart wrote on 2017-12-15 EST

I think it's pretty much hopeless for anyone to argue that humans can win against computers in any type of game. Our only chance of winning a game is to play it before it gets studied by computers. So people like @Aurelian and @JoeJoyce will need to stay busy inventing new games faster then people like @GregStrong and @HGMuller can program this stuff!!"

I actually agree that AI on good hardware will generally outperform humans, eventually. And for games, I suspect the AI will start with something very like AlphaZero as described by HG Muller below. If not, it will be something better.

I do think, however, people grossly underestimate the size of the game space AZ must evaluate each turn for a more complex abstract, or just how much the possibilities expand with each additional ply investigated. The ‘best moves’ often depend on enemy intentions and *exactly* where each piece winds up in 2 – 3 turns, and may depend on which order you move your 50 or 100 or 250… pieces each turn.

"H. G. Muller wrote on 2017-12-15 EST

Note that AlphaZero is not just a neural network. It is a tree search guided by a NN, the NN being also used for evaluation in the leaf nodes. The tactical abilities are mainly dependent on the search. The NN is just good at deciding which positions require further search to resolve the tactics."

The key to how well the AI does on commercially available machines in a few years (under reasonable assumptions) depends heavily on just how good the neural net is “at deciding which positions require further search to resolve the tactics,” I believe. That may be enough of a handicap for humans for a little while.

 

“Aurelian Florea wrote on 2017-12-16 EST

Actually I'm more in it for the mathematics of chess variants…”

Grin, that comment may have been a mistake! I would truly like to understand just why the Command and Maneuver games I’ve designed work as well as they do. In considering the introductory scenario A Tale of Two Countries: Intro, the first thing I noticed was that there are an amazing number of essentially equivalent moves available each turn, of which the player can only make 8. Which 8? It’s a small game, 12 x 24, with only 36 pieces/side at start, and while there are replacements and reinforcements arriving during the game, 36 units is probably the largest size either army will ever be.

 

I totally accept for the sake of argument that the AI will be a tactical genius in Tale, but I question the strategic elements because it seems to me that future game states are indeterminate, because while the AI may/will make the best tac moves this turn, the human probably won’t. So how does the AI ‘guess’ the game state in 2 or 3 turns, say 3 – 6 plys (player turns) deep?

 

In Macysburg, the situation is probably worse, at 32 x 32 and 84 pieces/side, all able to move each turn, arriving in 4 even-sized groups around the edge of the board over 20 turns, with ‘rally” allowing 1/3rd of the captured pieces to be returned to the board.  

 

The pieces dance back and forth seeking advantage. Where a piece is on the next turn is often difficult to determine. And ‘combat,’ standard chess capture, is totally dependent on the exact locations of every piece. While you can figure out/guess some of what your opponent might do in reply to your current moves, you really can’t do predictions accurate enough to put your pieces in motion for a couple turns and expect to have them all positioned right to demolish the enemy without taking equal losses.

 

For humans, there’s a very strong indeterminacy that provides the necessary ‘fog of war’ in the game. Why would the AI do so much better at penetrating that indeterminacy?

 

When I considered the paths - world lines - of the pieces in Tale, I saw that they were chaotic in the same sorts of ways that mathematical chaos is explained for the non-mathematical mind. Some strategically or tactically located pieces of terrain act as strange attractors, pulling in pieces from all over the board. Pieces that start off next to each other may all follow the same general (parallel) world line or split apart to end up almost anywhere on the board. And starting with the same board configuration, you may get some similar world lines from game to game, or wildly divergent ones.

 

Agreed, just in this description, I’ve given handles with which to attack the problem, and good statistics helps - a lot, I’d imagine. But isn’t there some sort of limit to how accurate a projection an AI could make? If AIs could truly predict the future, there’d be an awful lot of very rich programmers, no? ;) Doesn’t the strong presence of chaos wash away the ability to predict accurately? And isn’t that the AIs best weapon?

 

Finally, just for the record, the games I’m describing I’ve designed only because I wanted to play them, not to defeat computer players. I’ve long been fascinated by the idea of a  genuine, workable fusion of chess and war games, and for humans at least, these games work well, according to the people who managed to play them with me (some discussion on boardgamegeek.)