Evolution of Human-Competitive Agents in Modern Computer Games

Priesterjahn, Steffen; Krämer, Oliver; Weimer, Alexander; Goebels, Andreas

doi:10.1109/cec.2006.1688390

Cited by 29 publications

(21 citation statements)

References 5 publications

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“…An improvement would be to perform experiments against other AI-bots or human players. Also, in the event that no training AI-bot was present, it raises the question of whether co-evolution [12] could have been applied. DEFCON is a non-deterministic game, especially when involving human players.…”

Section: Discussionmentioning

confidence: 99%

“…The application of genetic programming has seen positive results in the fields such as robotic games [10] and board games like Chess [6], as well as racing track generation [13]. It has also been used to evolve human-competitive artificial players for Quake3 [12], and real-time strategy games [5]. By investigating the feasibility of applying an evolutionary approach with behaviours trees to develop a competitive automated AI player for a commercial game, we hope to further exemplify it as a viable means of AI development that may be adopted by the video games industry.…”

Section: Introductionmentioning

confidence: 99%

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Evolving Behaviour Trees for the Commercial Game DEFCON

Lim

Baumgarten

Colton

2010

Applications of Evolutionary Computation

103

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Abstract. Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game's original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolving Behaviour Trees for the Commercial Game DEFCON

Lim

Baumgarten

Colton

2010

Applications of Evolutionary Computation

103

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“…For learning partial gameplay, Overholtzer and Levy [2] used hardcoded elements, while Kadlec et al [3], Karpov et al [4], Parker and Bryant [5], Priesterjahn [6] and Thurau et al [7] addressed different parts of the controller using primitive actions. Cole et al [8] learned controllers for the full gameplay task using some hardcoded elements, like Small and Congdon [9] and Westra [10], while McPartland and Gallagher [11] used only primitive actions, though in a purpose-built FPS.…”

Section: A Game Ai and CI In First-person Shootersmentioning

confidence: 99%

Hierarchical controller learning in a First-Person Shooter

Hoorn

Togelius

Schmidhuber

2009

2009 IEEE Symposium on Computational Intelligence and Games

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Abstract-We describe the architecture of a hierarchical learning-based controller for bots in the First-Person Shooter (FPS) game Unreal Tournament 2004. The controller is inspired by the subsumption architecture commonly used in behaviourbased robotics. A behaviour selector decides which of three sub-controllers gets to control the bot at each time step. Each controller is implemented as a recurrent neural network, and trained with artificial evolution to perform respectively combat, exploration and path following. The behaviour selector is trained with a multiobjective evolutionary algorithm to achieve an effective balancing of the lower-level behaviours. We argue that FPS games provide good environments for studying the learning of complex behaviours, and that the methods proposed here can help developing interesting opponents for games.

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“…In the past, researchers have evolved agents for the video game Quake3, a 3-dimensional multiplayer game [2]. They used genetic algorithms to evolve an agent that preformed better than the bot provided with the game, and then went on to co-evolve opponents to use instead of the provided bot.…”

Section: Introductionmentioning

confidence: 99%

Evolving expert agent parameters for capture the flag agent in Xpilot

Parker

Penrose

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Xpilot is an open source, 2d space combat game. Xpilot-AI allows a programmer to write scripts that control an agent playing a game of Xpilot. It provides a reasonable environment for testing learning systems for autonomous agents, both video game agents and robots. In previous work, a wide range of techniques have been used to develop controllers that are focused on the combat skills for an Xpilot agent. In this research, a Genetic Algorithm (GA) was used to evolve the parameters for an expert agent solving the more challenging problem of capture the flag.

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Evolution of Human-Competitive Agents in Modern Computer Games

Cited by 29 publications

References 5 publications

Evolving Behaviour Trees for the Commercial Game DEFCON

Evolving Behaviour Trees for the Commercial Game DEFCON

Hierarchical controller learning in a First-Person Shooter

Evolving expert agent parameters for capture the flag agent in Xpilot

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