General game playing

History

In 1992, Barney Pell defined the concept of Meta-Game Playing and developed the "MetaGame" system. This was the first program to automatically generate chess-like game rules, and one of the earliest programs to use automated game generation. Pell then developed the system Metagamer. This system was able to play a number of chess-like games, given game rules definition in a special language called Game Description Language (GDL), without any human interaction once the games were generated.

In 1998, the commercial system Zillions of Games was developed by Jeff Mallett and Mark Lefler. The system used a LISP-like language to define the game rules. Zillions of Games derived the evaluation function automatically from the game rules based on piece mobility, board structure and game goals. It also employed usual algorithms as found in computer chess systems: alpha–beta pruning with move ordering, transposition tables, etc. The package was extended in 2007 by the addition of the Axiom plug-in, an alternate metagame engine that incorporates a complete Forth-based programming language.

In 1998, z-Tree was developed by Urs Fischbacher. z-Tree is the first and the most cited software tool for experimental economics. z-Tree allows the definition of game rules in z-Tree-language for game-theoretic experiments with human subjects. It also allows definition of computer players, which participate in a play with human subjects.

In 2005, the Stanford Project General Game Playing was established.

In 2012, the development of PyVGDL started.

GGP implementations

Stanford project

General Game Playing is a project of the Stanford Logic Group of Stanford University, California, which aims to create a platform for general game playing. It is the most well-known effort at standardizing GGP AI, and generally seen as the standard for GGP systems. The games are defined by sets of rules represented in the Game Description Language. In order to play the games, players interact with a game hosting server that monitors moves for legality and keeps players informed of state changes.

Since 2005, there have been annual General Game Playing competitions at the AAAI Conference. The competition judges competitor AI's abilities to play a variety of different games, by recording their performance on each individual game. In the first stage of the competition, entrants are judged on their ability to perform legal moves, gain the upper hand, and complete games faster. In the following runoff round, the AIs face off against each other in increasingly complex games. The AI that wins the most games at this stage wins the competition, and until 2013 its creator used to win a $10,000 prize. So far, the following programs were victorious:

Other approaches

Other general game playing software that use their own languages for defining game rules include:

GVGP implementations

Reinforcement learning

GVGP could potentially be used to create real video game AI automatically, as well as "to test game environments, including those created automatically using procedural content generation and to find potential loopholes in the gameplay that a human player could exploit". GVGP has also been used to generate game rules, and estimate a game's quality based on Relative Algorithm Performance Profiles (RAPP), which compare the skill differentiation that a game allows between good AI and bad AI.

Video Game Description Language

The General Video Game AI Competition (GVGAI) has been running since 2014. In this competition, two-dimensional video games similar to (and sometimes based on) 1980s-era arcade and console games are used instead of the board games used in the GGP competition. It has offered a way for researchers and practitioners to test and compare their best general video game playing algorithms. The competition has an associated software framework including a large number of games written in the Video Game Description Language (VGDL), which should not be confused with GDL and is a coding language using simple semantics and commands that can easily be parsed. One example for VGDL is PyVGDL developed in 2013.

Algorithms

Since GGP AI must be designed to play multiple games, its design cannot rely on algorithms created specifically for certain games. Instead, the AI must be designed using algorithms whose methods can be applied to a wide range of games. Recent GGP systems such as Regular Boardgames (RBG) and Ludii have explored alternative rule representations to optimize reasoning efficiency and support a broader variety of games. The AI must also be an ongoing process, that can adapt to its current state rather than the output of previous states. For this reason, open loop techniques are often most effective.{{Cite journal|title=Recent Advances in General Game Playing|journal=The Scientific World Journal|date=2015 |volume=2015 |publisher= Hindawi Publishing Corporation

A popular method for developing GGP AI is the Monte Carlo tree search (MCTS) algorithm. Often used together with the UCT method (Upper Confidence Bound applied to Trees), variations of MCTS have been proposed to better play certain games, as well as to make it compatible with video game playing. Another variation of tree-search algorithms used is the Directed Breadth-first Search (DBS), in which a child node to the current state is created for each available action, and visits each child ordered by highest average reward, until either the game ends or runs out of time. In each tree-search method, the AI simulates potential actions and ranks each based on the average highest reward of each path, in terms of points earned.

Assumptions

In order to interact with games, algorithms must operate under the assumption that games all share common characteristics. In the book Half-Real: Video Games Between Real Worlds and Fictional Worlds, Jesper Juul gives the following definition of games: Games are based on rules, they have variable outcomes, different outcomes give different values, player effort influences outcomes, the player is attached to the outcomes, and the game has negotiable consequences. Using these assumptions, game playing AI can be created by quantifying the player input, the game outcomes, and how the various rules apply, and using algorithms to compute the most favorable path.

References

References

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