I am afraid I have to push the ‘Go Engines Analysis’ post to a later date.
Commercial and Academic Research in Games
Generally, most research done by academics is of some use commercially. And naturally, one would expect the same to be the case in game AI research also. Only, its not. Commercial games concentrate more on the graphics, and the user experience part of the game, when compared to AI. If there is a bold announcement from the industry stating that the next edition of their game is to have the ‘next-generation-human-like-AI’, they have mostly been huge let-downs, and miserable failures. The strongest AI, in commercial games, these days are those that ‘cheat’. Have you never wondered in-game, at least in the harder levels, ‘When did he get so much points?’,’Does his nitrous never empty?’. And sometimes just plain ‘WTF!!’
Instead, the NPCs ( Non Player Characters ) in the majority of games are controlled by finite state machines, lists of conditional statements, and the occasional A* path finding algorithm. Typically, no learning is present in either development or execution of the AI. Everything is hard-coded by the human game developers.
Academic Research into game AI hasn’t been too rewarding either. So we built the strongest Chess player in history. And we are building better ones. Has that helped us understand how people play the game? How we learn to play the game? How we interpret patterns? Also, Academic Research is mainly focussed on making AI increasingly better at the game, aiming to make it the best. Naturally, that’s not the commercial requirement. Would anyone but Kasprov want to play Deep Blue? What is required commercially is that the player’s opponents should neither be too domineering, nor too easy.
How Games interest us
According to Raph Koster, a game is fun to play because we learn the game as we play; we understand and learn the patterns underlying the game, and finally “grok” how to play it. This requires that the level of challenge always is approximately right, and that new patterns are always available to learn – games that are too simple or impossible to understand are boring.
Another theorist who has tried to nail down the essence of games and why they are fun is Thomas Malone. He claims that the factors that make games fun can be organized into three categories: challenge, fantasy, and curiosity.
The existence of some sort of goal adds to the entertainment value. Further, this goal should not be too hard or too easy to attain, and the player should not be too certain about what level of success he will achieve.
Games that include fantasy, according to Malone, show or evoke images of physical objects or social situations not actually present. The sensation of being somewhere else, being someone else, doing something else.
As for the third factor, curiosity, Malone claims that fun games have an optimal level of informational complexity in that their environments are novel and surprising but not completely incomprehensible. These are games that invite exploration, and keeps the user playing just to see what happens next.
The most interesting applications of real Computational Intelligence and Machine Learning methods
NERO, is a fantastic new concept. The player ‘teaches’ (initially stupid) bots to become ‘trained’ soldiers. In the core of the game is a learning system called NEAT. It involves using Genetic Algorithms to train Artificial Neural Networks( NeuroEvolution ). NEAT involves continually taking ANNs, from an initially random population, and then combining the best among them in the hope that it’ll result in a better ANN.
Another place where learning methods may be put to use is Automatic Content Generation. Here is an intriguing new concept. Customized racing tracks according to a player’s skill. The next Prince of Persia edition claims, custom player experience. Although it is not clear of how ‘custom’ it is to be, this sounds like the beginning of something big. So Automatic Content Generation is definitely IN.
Another instance of Content Generation, in the process of development, is a game called Galactic Arms Race, being developed by Ken Stanley, and his team at UCF, the same dude who pioneered NERO. Remember the classic, Space Invader arcade game? It seems to be something similar except, there is a large and INCREASING variety of guns you can choose from. And each gun fires differently. That is, the graphics is different. And the graphics was not coded by hand, it was EVOLVED by a custom version of NEAT.
GECCO 2009, is to conduct two competitions, Salamander and Car Racing. The details of Salamander are not too clear to me, but looking at this and this, it seems, the Salamander is to learn to walk and then catch flies around the house.
The car racing controllers are tested on TORCS. (TORCS, does not have dynamite graphics, but I find it a lot more difficult than NFS, but thats just my perception. It was built mainly for testing AI conntollers). Last year’s entries were quite a variety. There was a controller based on NEAT, and there was also a hand coded controller(Although, I doubt it performed as well as the other trained/evolved controllers, score CI/ML!). The significance of a trained controller is that, provided it is trained sufficiently well on a good track, it acquires the basic driving skills. This would be a big plus for games like Trackmania Nations, which allow the gamer to design his own tracks, but does not give competing cars, leaving the gamer to look for human opponents, to race in his own track. Another aspect of trained units is the versatility. A small change in the training process and the controllers become more aggressive, trying more to push the other players off the track!!
The crux of the matter. If you were to host a contest, which game, do you think, considering the expanding scope for computational intelligence in games illustrated by the examples above, would be a good bed for innovative application of CI/ML?
Remember, its a time bound contest. The participant should have enough to time to experiment.