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> but when we talk about "games" in AI we do mean the classics Only because of inertia. There's nothing inherently special about "classics". Eventually somebod

> but when we talk about "games" in AI we do mean the classics Only because of inertia. There's nothing inherently special about "classics". Eventually somebody will branch out once Go and poker are mined out of paper and article opportunity. Once we do then maybe some new, interesting algorithms will be found. In principle, every game can be solved by storing all possible game states in a database. Where brute-force storing is impractical due to size concerns, compression tricks have to be used. E.g., Go is a simple game because at the end, every one of the fixed number of board spaces is either +1, -1 or 0. Add them up and you know if you won. This means that every move is either "correct" or "incorrect"; the problem of classifying multidimensional objects into two classes is a problem that we're pretty good at now, and things like neural networks get the job done. A slightly more complex game like Agricola has no "correct" and "incorrect" moves because it's not zero-sum; you can make an "incorrect" move and still win as long as your opponent is forced to make a relatively more "incorrect" move. Not sure how much of a difference that makes, but what's certain is that by (effectively) solving Go we've only scratched the surface. It's not the end of research, only the beginning.

YeGoblynQueenne 9y ago
Sure. Research in game playing AI doesn't end with Go, or any other game. We may see more research in modern board games, now that we're slowly running out of the classics. I think you're underestimating the amount of work and determination it took to get to where we are today, though (I mean your comment about "inertia"). Classic board games have the advantage of a long history and of being well understood (the uncertainty about optimal strategies in Go notwithstanding). Additionally, for at least some of them like chess, there are rich databases of entire games that can be used outright, without the AI player having to generate-and-test them in the process of training or playing. The same is not true for modern games. On the one hand, modern board games like Agricola (or, dunno, Settlers or Carcassonne etc) don't have such an extensive and multi-national following as the classics so it's much harder to find a lot of data to train on (which is obviously important for machine-learning AI players). I had that problem when considering an M:tG AI trained with machine learning: I would have liked to find play-by-play data on professional games but there just isn't any (or where there is it's not enough, or it's not in any standardised format). Finally, classic board games have cultural significance that modern board games dont' quite match, despite the huge popularity of CCGs like M:tG or Pokemon, or Eurogame hits like Settlers. Go, chess and backgammon in particular have tremendous historical significance in their respective areas of the world- chess in Eastern Europe, backgammon in the Middle East, Go in SE Asia. People go to special academies to learn them, master players are widely recognised etc. You don't get that level of interest with modern board games- so there's less research interest for them, also. People in game playing AI have been trying for a very long time to crack some games like Go and, recently, poker (not quite cracked yet). They didn't sit around twiddling their thumbs all those years, neither did they choose classical board games over modern ones just because they didn't have the imagination to think of the latter. In AI research, as in all research, you have to make progress before you can make more progress.

YeGoblynQueenne 9y ago

Sure. Research in game playing AI doesn't end with Go, or any other game. We may see more research in modern board games, now that we're slowly running out of the classics. I think you're underestimating the amount of work and determination it took to get to where we are today, though (I mean your comment about "inertia"). Classic board games have the advantage of a long history and of being well understood (the uncertainty about optimal strategies in Go notwithstanding). Additionally, for at least some of them like chess, there are rich databases of entire games that can be used outright, without the AI player having to generate-and-test them in the process of training or playing. The same is not true for modern games. On the one hand, modern board games like Agricola (or, dunno, Settlers or Carcassonne etc) don't have such an extensive and multi-national following as the classics so it's much harder to find a lot of data to train on (which is obviously important for machine-learning AI players). I had that problem when considering an M:tG AI trained with machine learning: I would have liked to find play-by-play data on professional games but there just isn't any (or where there is it's not enough, or it's not in any standardised format). Finally, classic board games have cultural significance that modern board games dont' quite match, despite the huge popularity of CCGs like M:tG or Pokemon, or Eurogame hits like Settlers. Go, chess and backgammon in particular have tremendous historical significance in their respective areas of the world- chess in Eastern Europe, backgammon in the Middle East, Go in SE Asia. People go to special academies to learn them, master players are widely recognised etc. You don't get that level of interest with modern board games- so there's less research interest for them, also. People in game playing AI have been trying for a very long time to crack some games like Go and, recently, poker (not quite cracked yet). They didn't sit around twiddling their thumbs all those years, neither did they choose classical board games over modern ones just because they didn't have the imagination to think of the latter. In AI research, as in all research, you have to make progress before you can make more progress.