Using a genetic algorithm to tune first-person shooter bots

Cole, Nancy S.; Louis, Sushil J.; Miles, Christopher E.

doi:10.1109/cec.2004.1330849

Cited by 84 publications

(42 citation statements)

References 4 publications

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“…Khoo (2002) developed an inexpensive AI technique based on the well-known Eliza program (Weizenbaum 1966), so that users get the impression of playing against humans instead of bots. In Cole et al (2004), the parameters of the Counter-Strike built-in weapon selection rules are tuned by using artificial evolution. Furthermore, there have been attempts to mimic human behavior offline, from samples of human playing, in a specific virtual environment.…”

Section: Learning In Gamesmentioning

confidence: 99%

Towards Optimizing Entertainment in Computer Games

Yannakakis

Hallam

2007

Applied Artificial Intelligence

101

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& Mainly motivated by the current lack of a qualitative and quantitative entertainment formulation of computer games and the procedures to generate it, this article covers the following issues: It presents the features-extracted primarily from the opponent behavior-that make a predator=prey game appealing; provides the qualitative and quantitative means for measuring player entertainment in real time, and introduces a successful methodology for obtaining games of high satisfaction. This methodology is based on online (during play) learning opponents who demonstrate cooperative action. By testing the game against humans, we confirm our hypothesis that the proposed entertainment measure is consistent with the judgment of human players. As far as learning in real time against human players is concerned, results suggest that longer games are required for humans to notice some sort of change in their entertainment.Intelligent interactive opponents can provide more enjoyment to a vast gaming community of constant demand for more realistic, challenging, and meaningful entertainment (Fogel et al. 2004;Champandard 2004). However, given the current state-of-the-art in artificial intelligence (AI) in computer games, it is unclear which features of any game contribute to the satisfaction of its players, and thus it is also uncertain how to develop enjoyable games. Because of this lack of knowledge, most commercial and academic research in this area is fundamentally incomplete. The challenges we consider in this article are to provide qualitative and quantitative means for distinguishing a game's enjoyment value and to develop efficient AI tools to automatically generate entertainment for the player.In our previous work (Yannakakis and Hallam 2004), we defined criteria that contribute to the satisfaction for the player, which map toThe authors would like to thank the anonymous reviewers for their valuable feedback and the players for their vital contribution to this work.

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Section: Learning In Gamesmentioning

confidence: 99%

Towards Optimizing Entertainment in Computer Games

Yannakakis

Hallam

2007

Applied Artificial Intelligence

101

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“…The job of tuning parameters can be time consuming due to the large number of them. Some researchers have attempted to tune these parameters using genetic algorithms [4] [7][12] [13]. An FPS environment was used with the parameters being the behaviour of the bots [12][13] [4].…”

Section: Introductionmentioning

confidence: 99%

Game Designers Training First Person Shooter Bots

McPartland

Gallagher

2012

Lecture Notes in Computer Science

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Abstract. Interactive training is well suited to computer games as it allows game designers to interact with otherwise autonomous learning algorithms. This paper investigates the outcome of a group of five commercial first person shooter game designers using a custom built interactive training tool to train first person shooter bots. The designers are asked to train a bot using the tool, and then comment on their experiences. The five trained bots are then pitted against each other in a deathmatch scenario. The results show that the training tool has potential to be used in a commercial environment.

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“…For example, there has been particular research interest in the creation of adaptive interactive multi-agent first-person shooter games (Cole, Louis, & Miles, 2004), (Hong & Cho, 2004), (Stanley et al, 2005b), as well as strategy games (Bryant & Miikkulainen, 2003), (Revello & McCartney, 2002), (Yannakakis, Levine, & Hallam, 2004) using artificial evolution and learning as design methods for agent behavior. Research work has primarily focused on the derivation of game playing multi-agent strategies, using either online or off-line adaptation methods in both continuous and discrete multi-agent games (Agogino, Stanley, & Miikkulainen, 2000), (Bryant & Miikkulainen, 2003), (Stanley & Miikkulainen, 2004), (Moriarty & Miikkulainen, 1995), (Moriarty & Miikkulainen, 1996), (Richards, Moriarty, McQuesten, & Miikkulainen, 1997).…”

Section: Multi-agent Computer Gamesmentioning

confidence: 99%

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Nitschke

Schut

Eiben

2008

Intelligent Complex Adaptive Systems

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Specialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles, where specialization that emerges as a result of system dynamics and is used problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research and future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies. 2 Emergent Specialization in Biologically Inspired Collective Behavior Systems AbstractSpecialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles, where specialization that emerges as a result of system dynamics and is used problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research and future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies.

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Using a genetic algorithm to tune first-person shooter bots

Cited by 84 publications

References 4 publications

Towards Optimizing Entertainment in Computer Games

Towards Optimizing Entertainment in Computer Games

Game Designers Training First Person Shooter Bots

Emergent Specialization in Biologically Inspired Collective Behavior Systems

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