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General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. 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.
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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, or are predefined manually in a domain-specific language and sent in advance to artificial players 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 as well as a program that can learn to play Nintendo Entertainment System games.
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. 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.
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:
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. The games used in GVGP are, for now, often 2-dimensional arcade games, as they are the simplest and easiest to quantify. To simplify the process of creating an AI that can interpret video games, games for this purpose are written in VGDL manually. 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.
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.
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.
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.
As gaming has gained exposure to a wider audience and increasingly become part of the cultural mainstream, the content of games themselves has come under increased scrutiny. To test public attitudes toward some of these ongoing arguments, the survey presented Americans with some potential impacts of games and asked whether they consider these attributes to be true of most games, not true of most games, or whether they apply to some games but not others.
Orthographic camera: A camera view that makes objects appear fixed on the screen, regardless of their actual distance from one another or their relative positions. This is commonly used for retro-style 2D games, as it can make GameObjects look flat, or 2.5D games (2D games that utilize 3D elements), specifically because they allow for touches of 3D depth and definition while maintaining an otherwise 2D appearance.
Augmented reality (AR): An experience that combines gameplay with augmented reality features overlaid on a physical location. Examples of mobile AR games include Pokémon Go and Jurassic World Alive.
Others think this comparison to gambling is flawed because there may not be financial or material losses involved with playing video games. In addition, winning a video game may require cognitive skills and sharp reflexes, while winning at gambling is mainly a matter of chance.
So far, researchers think the process of playing and winning video games may trigger a release of dopamine. Dopamine is a brain chemical (neurotransmitter) that plays a key role in several bodily functions, including pleasurable reward and motivation. Dopamine is the same neurotransmitter involved in other use disorders, including gambling disorder and substance use disorder.
In fact, video games did not get their true start from computer programmers, but from an engineer skilled in another major invention of the 20th century: the television set. By the 1960s, millions of Americans had invested in televisions for their homes, but these television sets were only used for the viewing of entertainment. Engineer Ralph Baer was certain this technology could be used to play games.
The home version of Pong was just as successful as the arcade version. Atari sold 150,000 units in 1975 alone (compared to the 200,000 Odysseys that took Magnavox three years to sell.) Other companies soon began to produce their own home versions of Pong. Even Magnavox began to market a series of modified Odyssey units that played only their tennis and hockey games. Of these first-generation video game consoles, the most successful was Coleco Telstar, due in part to some luck and the help of Ralph Baer.
In a classic case of supply outpacing demand, too many games hit the market, and many were of inferior quality. Further complicating matters, there were too many video game consoles from which to choose. Beyond the flooded market, video games consoles now faced growing competition from computers.
Sales of video game consoles and cartridges plunged in 1983 and 1984. Many companies like Mattel and Magnavox discontinued their video game lines completely, while Atari, the leader in the field, struggled to remain afloat. Video games remained popular arcade features, but it seemed that the era of home video game systems had ended.
But in 1985, a small Japanese company proved just the opposite. That year, Nintendo released its Nintendo Entertainment System (NES), whose popularity and commercial success surpassed any previous game console. No longer a novelty, video games found a firm foothold mainstream American life, just as Ralph Baer had predicted they would.
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