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It has been suggested that this article should be split into a new article titled General video game playing . (discuss) (June 2023) |
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. [1] [2] [3] For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For instance, a chess-playing computer program cannot play checkers. General game playing is considered as a necessary milestone on the way to artificial general intelligence. [4]
General video game playing (GVGP) is the concept of GGP adjusted to the purpose of playing video games. For video games, game rules have to be either learnt over multiple iterations by artificial players like TD-Gammon, [5] or are predefined manually in a domain-specific language and sent in advance to artificial players [6] [7] like in traditional GGP. Starting in 2013, significant progress was made following the deep reinforcement learning approach, including the development of programs that can learn to play Atari 2600 games [8] [5] [9] [10] [11] as well as a program that can learn to play Nintendo Entertainment System games. [12] [13] [14]
The first commercial usage of general game playing technology was Zillions of Games in 1998. General game playing was also proposed for trading agents in supply chain management thereunder price negotiation in online auctions from 2003 on. [15] [16] [17] [18]
This section needs to be updated.(October 2021) |
In 1992, Barney Pell defined the concept of Meta-Game Playing, and developed the "MetaGame" system. This was the first program to automatically generate game rules of chess-like games, and one of the earliest programs to use automated game generation. Pell then developed the system Metagamer. [19] 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. [20]
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. [21] 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. [22] 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. [23]
In 2005, the Stanford Project General Game Playing was established. [3]
In 2012, the development of PyVGDL started. [24]
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 [25] [26] 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. [19] So far, the following programs were victorious: [27]
Year | Name | Developer | Institution | Ref |
---|---|---|---|---|
2005 | Cluneplayer | Jim Clune | UCLA | |
2006 | Fluxplayer | Stephan Schiffel and Michael Thielscher | Dresden University of Technology | [28] |
2007 | Cadiaplayer | Yngvi Björnsson and Hilmar Finnsson | Reykjavik University | [29] |
2008 | Cadiaplayer | Yngvi Björnsson, Hilmar Finnsson and Gylfi Þór Guðmundsson | Reykjavik University | |
2009 | Ary | Jean Méhat | Paris 8 University | |
2010 | Ary | Jean Méhat | Paris 8 University | |
2011 | TurboTurtle | Sam Schreiber | ||
2012 | Cadiaplayer | Hilmar Finnsson and Yngvi Björnsson | Reykjavik University | |
2013 | TurboTurtle | Sam Schreiber | ||
2014 | Sancho | Steve Draper and Andrew Rose | [30] | |
2015 | Galvanise | Richard Emslie | ||
2016 | WoodStock | Eric Piette | Artois University |
There are other general game playing systems, which use their own languages for defining the game rules. Other general game playing software include:
System | Year | Description |
---|---|---|
FRAMASI | 2009 | Developed for general game playing and economic experiments during a PhD thesis. [31] [32] |
AiAi | 2015-2017 | Developed by Stephen Tavener (previous Zillions developer). [33] [34] [35] |
PolyGamo Player | 2017 | Released by David M. Bennett in September 2017 based on the Unity game engine. [36] |
Regular Boardgames | 2019 | Developed by Jakub Kowalski, Marek Szykuła, and their team at University of Wrocław. [37] [38] |
Ludii | 2020 | Released by Cameron Browne and his team at Maastricht University as part of the ERC-funded Digital Ludeme Project. [39] [40] [41] |
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". [7] 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. [42]
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. [6] [24] The games used in GVGP are, for now, often 2-dimensional arcade games, as they are the simplest and easiest to quantify. [43] To simplify the process of creating an AI that can interpret video games, games for this purpose are written in VGDL manually.[ clarification needed ] VGDL can be used to describe a game specifically for procedural generation of levels, using Answer Set Programming (ASP) and an Evolutionary Algorithm (EA). GVGP can then be used to test the validity of procedural levels, as well as the difficulty or quality of levels based on how an agent performed. [44]
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. 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. [45]
A popular method for developing GGP AI is the Monte Carlo tree search (MCTS) algorithm. [46] 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. [47] [48] [49] Another variation of tree-search algorithms used is the Directed Breadth-first Search (DBS), [50] 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. [51] 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. [46] [51]
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. [52] 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. [43]
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of other living beings, primarily of humans. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.
Computer Go is the field of artificial intelligence (AI) dedicated to creating a computer program that plays the traditional board game Go. The field is sharply divided into two eras. Before 2015, the programs of the era were weak. The best efforts of the 1980s and 1990s produced only AIs that could be defeated by beginners, and AIs of the early 2000s were intermediate level at best. Professionals could defeat these programs even given handicaps of 10+ stones in favor of the AI. Many of the algorithms such as alpha-beta minimax that performed well as AIs for checkers and chess fell apart on Go's 19x19 board, as there were too many branching possibilities to consider. Creation of a human professional quality program with the techniques and hardware of the time was out of reach. Some AI researchers speculated that the problem was unsolvable without creation of human-like AI.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance.
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John Ross Quinlan is a computer science researcher in data mining and decision theory. He has contributed extensively to the development of decision tree algorithms, including inventing the canonical C4.5 and ID3 algorithms. He also contributed to early ILP literature with First Order Inductive Learner (FOIL). He is currently running the company RuleQuest Research which he founded in 1997.
Game Description Language (GDL), an innovation in the field of artificial intelligence, represents a significant step forward in the quest to create versatile game-playing AI agents. Designed by Michael Genesereth, GDL is a specialized logic programming language that finds its home within the ambitious realm of the General Game Playing Project at Stanford University. This project aims to develop AI agents capable of playing a wide variety of games without the need for game-specific programming.
The following outline is provided as an overview of and topical guide to artificial intelligence:
Artificial intelligence (AI) has been used in applications throughout industry and academia. Similar to electricity or computers, AI serves as a general-purpose technology that has numerous applications. Its applications span language translation, image recognition, credit scoring, e-commerce and various other domains.
There are a number of competitions and prizes to promote research in artificial intelligence.
Zillions of Games is a commercial general game playing system developed by Jeff Mallett and Mark Lefler in 1998. The game rules are specified with S-expressions, Zillions rule language. It was designed to handle mostly abstract strategy board games or puzzles. After parsing the rules of the game, the system's artificial intelligence can automatically play one or more players. It treats puzzles as solitaire games and its AI can be used to solve them.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree.
DeepMind Technologies Limited, doing business as Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014, The company is based in London, with research centres in Canada, France, Germany and the United States.
AlphaGo is a computer program that plays the board game Go. It was developed by the London-based DeepMind Technologies, an acquired subsidiary of Google. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as AlphaGo Zero, which was completely self-taught without learning from human games. AlphaGo Zero was then generalized into a program known as AlphaZero, which played additional games, including chess and shogi. AlphaZero has in turn been succeeded by a program known as MuZero which learns without being taught the rules.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
Explainable AI (XAI), often overlapping with Interpretable AI, or Explainable Machine Learning (XML), either refers to an AI system over which it is possible for humans to retain intellectual oversight, or to the methods to achieve this. The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.
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Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control and procedural content generation (PCG). Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems.
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