Two-Player Game AI
2016
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Abstract
AI
AI
This project focuses on the development of artificial intelligence (AI) for two-player games, specifically tic-tac-toe and rock, paper, scissors. By implementing simple game mechanics, the project aims to enhance understanding of AI functionality and improve strategic capabilities of game-playing AIs. Currently, the tic-tac-toe AI achieves optimal performance, often resulting in draws against itself, while future improvements are planned to incorporate more advanced algorithms and predictive strategies for the rock, paper, scissors AI.
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This paper presents how artificial intelligence (AI) is used in computer games to solve common problems and provide game features. Specifically, non-playing character (NPC) path finding, decision making and learning are examined. Different AI techniques are looked at as to how they help provide a solution to these problems and features in computer games. This discussion is followed by a survey of research articles regarding the different type of AI techniques presented.
IEEE Transactions on Computational Intelligence and AI in Games, 2009
2003
With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games.
Proceedings of IADIS International Conference on Game and Entretainment Technologies. Freiburg, Germany, 2010
Usually, when we talk about computer games and artificial intelligence, we are talk about how the artificial intelligence is used in order to simulate intelligence in characters that appear in computer games. This kind of artificial intelligence is quite limited. Our proposal in this paper is to explore other possibilities in this interesting field, in which we will show two different proposals. The first one is using artificial intelligence in order to create adaptive computer games. The second one is to use computer games to train artificial intelligence programs. We present an example for each concept; a survey analysing the behaviour of players of Pac-Man, and an association game exploring the synesthetic relationship of images, sounds and text.
Eludamos. Journal for Computer Game Culture, 2019
In 2006, the first-person shooter F.E.A.R. makes headlines in the gaming world. One feature in particular attracts much attention: the non-playable characters seem to behave intelligently to a degree yet unseen in computer games. From earlier productions like No One Lives Forever 1 & 2 (2000, 2002), players were already familiar with NPCs that are able to seek cover under fire and to leave it at random in order to shoot back at the player. In F.E.A.R. that happens too, but in a much more realistic manner. Computer-controlled enemies attack players in a coordinated way. If one member of the enemy team comes closer, he gets supportive fire by his team members. If the player attacks them, enemy forces remain in cover until they are immediately threatened. Ten years later, an AI system called AlphaGo beats the human world champion Kim Sung Yong in the ancient board game Go in five rounds—final score: 4-1. The global community of Go players is perplexed, almost shocked, even though the victory did not totally come out of the blue. Already in October 2015, an earlier version of AlphaGo was able to beat the European Go champion Fan Hui. However, Hui’s playing level was significantly lower than that of Kim Sung Yong (2-dan out of possible 9-dan levels). As these introductory examples illustrate, the relationship between artificial intelligence (AI) and games can basically be studied from two perspectives: The first is the implementation of AI technologies in games, in order to improve the game experience in one way or another, for example with the intention to make it more believable, more immersive, or simply more enjoyable. The second is the use of games as a benchmark, a learning or test environment to evaluate, but also demonstrate, the current state of AI technologies. Both perspectives have gained enormous importance in recent years—technically, but also culturally and economically.
Aaai, 1999
Computer programs now play many board games as well or better than the most expert humans. Human players, however, learn, plan, allocate resources, and integrate multiple streams of knowledge. This paper highlights recent achievements in game playing, describes some cognitively-oriented work, and poses three related challenge problems for the AI community. Game Playing as a Domain Work on games has had several traditional justifications. Given unambiguous rules, playing a game to win is a well-defined problem. A game's rules create artificial world states whose granularity is explicit. There is an initial state, a state space with clear transitions, and a set of readily describable goal states. Without intervening instrumentation, games are also noise-free. For these reasons, as well as for their ability to amuse, games have often been referred to as "toy domains." To play the most difficult games well, however, a program must contend with fundamental issues in AI: knowledge representation, search, learning, and planning. There are two principal reasons to continue to do research on games, despite Deep Blue's triumph (Hamilton and Hedberg 1997). First, human fascination with game playing is long-standing and pervasive. Anthropologists have catalogued popular games in almost every culture. Indeed, the same game, under various names, often appears on many continents (Bell 1969; Zaslavsky 1982). Games intrigue us because they address important cognitive functions. In particular, the games humans like to play are probably the ones we are good at, the ones that capitalize on our intellectual strengths and forgive our weaknesses. A program that plays many games well must simulate important cognitive skills. The second reason to continue game-playing research is that some difficult games remain to be won, games that people play very well but computers do not. These games clarify what our current approach lacks. They set challenges for us to meet, and they promise ample rewards. This paper summarizes the role of search and knowledge in game playing, the state of the art, and recent relevant data on expert human game players. It then shows how cognitive skills can enhance a game-playing program, and poses three new challenge problems for the AI community. Although rooted in game playing, these challenges could enhance performance in many domains.
Abstract This report documents the program and the outcomes of Dagstuhl Seminar 12191 “Artificial and Computational Intelligence in Games”. The aim for the seminar was to bring together creative experts in an intensive meeting with the common goals of gaining a deeper understanding of various aspects of artificial and computational intelligence in games, to help identify the main challenges in game AI research and the most promising venues to deal with them.
Communications of the ACM, 2002
Presented are issues in designing smart, believable software agents capable of playing strategy games, with particular emphasis on the design of an agent capable of playing Cyberwar XXI, a complex war game. The architecture of a personality-rich, advise-taking game playing agent that learns to play is described. The suite of computational-intelligence tools used by the advisers include evolutionary computation and neural nets.

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