AI System Invents New Card Games (For Humans)

jtogel writes "This New Scientist article describes our AI system that automatically generates card games. The article contains a description of a playable card game generated by our system. But card games are just the beginning... The card game generator is a part of a larger project to automatise all of game development using artificial intelligence methods — we're also working on level generation for a variety of different games, and on rule generation for simple arcade-like games."

Comments

Good luck with that

[gweihir]

Creating games or levels is pretty simple (well, relatively speaking) in comparison to making them fun. Bu the myriad of bad games out there, I would say that making good games or levels is something not even natural intelligence masters routinely. It is bound to fail trying to do it with AI. Nonetheless a nice research benchmark. But please stop trying to imply real-world usability where there is none. It is unethical and unprofessional.

The rules from the TFA ...

[TrollstonButtersbean]

Each player gets six cards, except for the player on the dealer's right, who gets seven. .. Two jacks are a "half-fizzbin". If you have a half-fizzbin: a third jack is a "shralk" and results in disqualification. One wants a king and a deuce, except at night ...when one wants a queen and a four. The second card is turned up, except on Tuesdays.

Re: Machine learning game strategies

[clam666]

We use several forms of evolutionary programming in several sections of the learning systems' areas.

There are hybridized genetic algorithms in the portions involving the strategy blending evolution system, which does a few different forms of strategy selection pressure and evolution controls, which is critical due to training time to not cause premature convergence or genetic instability.

Additionally, we introduce additonal factors such a genetic drift and migration so that out competing strategies can evolve independently as the explore the strategy plane.

There are macro level evolution techniques to handle the complexity growth of the strategy species, so that the complexity can be altered depending on how "advanced" the system needs to be. In a simple sense of a turn based game, it would equate to the number of plies or analysis depth you would go. For more complex multiobjective systems, like military tactics involving minimizing casualties, civilian losses, maximizing kill or capture of enemy units, minimizing structural damage to infrastructure, etc., then it modifies the strategy complexity. For example, you could send eveyone with guns to kill everyone, or you could parallel it on intelligence gathering with drone units to direct fire, long range snipers or diversionary tactics, or factoring logistical support costs.

A lot of the core work is maximizing the efficiency of the evolutionary strategies, as they are the biggest fator in learning time. It's really easy to write inefficient logic that ends up taking much longer to arrive at good solutions without getting lost due to too much noise or oscillation in the system.

Another method that is used is a version of PSO, which is used to optimize subsections of the strategy (depending on what we are trying to find a solution to) that further get to optimal solutions.

So a lot of bachelors level CS is used. Although a lot of customization has been done, the benefit is it uses a lot of basic concepts, and utilized processing power rather than trying to algorithmically come up with solutions. Also, it can be continuously adaptable so it adjusts to situational changes. The strategy isn't locked, it can be reacts based on changes to frontier so to speak. If your opponent changes what they're doing, or doing something new, it can adjust itself to that.