Optimizing player behavior in a real-time strategy game using evolutionary algorithms

Fernández-Ares, Antonio; Mora, Antonio M.; Merelo, J. J.; García‐Sánchez, Pablo; Fernandes, Carlos M.

doi:10.1109/cec.2011.5949863

Cited by 20 publications

(11 citation statements)

References 16 publications

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“…Probably these aspects make these type of games less researched than their turn-based games counterparts [12], despite the great number of know open challenges such as: adversarial real-time planning, decision making under uncertainty of partially observable domains, learning from experience or observation, opponent modeling, spatial and temporal reasoning, navigation, resource management and collaboration [13], [14].…”

Section: B Artificial Intelligence In Rts Gamesmentioning

confidence: 99%

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Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

Tavares

Azpúrua

Chaimowicz

2014

2014 Brazilian Symposium on Computer Games and Digital Entertainment

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Real time strategy games are complex scenarios where multiple agents must be coordinated in a dynamic, partially observable environment. In this work, we model the coordination of these agents as a task allocation problem, in which specific tasks are given to the agents that are more suited to execute them. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using a genetic algorithm. To evaluate this approach, we implement this coordination mechanism in the AI of a popular video game: StarCraft: BroodWar. Experiment results show that the genetic algorithm enhances performance of the task allocation algorithm. Besides, performance of the proposed approach in matches against StarCraft's native AI is comparable to that of a tournament-level software-controlled player for StarCraft.

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Section: B Artificial Intelligence In Rts Gamesmentioning

confidence: 99%

“…An example of evolutionary algorithm applied to evolve parameters of a RTS game player can be found in [12].…”

Section: B Artificial Intelligence In Rts Gamesmentioning

confidence: 99%

Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

Tavares

Azpúrua

Chaimowicz

2014

2014 Brazilian Symposium on Computer Games and Digital Entertainment

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“…It is available online. 7 The GP Challenge dataset 8 was proposed as a GP benchmark by Widera et al [56]. The problem is to model the energy function of various proteins in terms of numerical input features.…”

Section: Symbolic Regressionmentioning

confidence: 99%

“…Others (Ms. Pacman 19 [12], AI Challenge 20 [8], Robocode 21 [20], and Battle Code 22 ) require head to head comparison of controllers. The advantage is obvious in a competition scenario: the difficulty of the problem increases as the quality of solutions improve.…”

Section: Planning and Controlmentioning

confidence: 99%

Better GP benchmarks: community survey results and proposals

White

McDermott

Castelli

et al. 2012

Genet Program Evolvable Mach

200

135

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We present the results of a community survey regarding genetic programming benchmark practices. Analysis shows broad consensus that improvement is needed in problem selection and experimental rigor. While views expressed in the survey dissuade us from proposing a large-scale benchmark suite, we find community support for creating a ''blacklist'' of problems which are in common use but have important flaws, and whose use should therefore be discouraged. We propose a set of possible replacement problems.

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“…Probably these aspects make real-time strategy games less researched than their turn-based counterparts [13], despite the great number of know open challenges such as: adversarial real-time planning, decision making under uncertainty of partially observable domains, learning from experience or observation, opponent modeling, spatial and temporal reasoning, navigation, resource management and collaboration [14], [15].…”

Section: B Artificial Intelligence In Rts Gamesmentioning

confidence: 99%

Combining genetic algorithm and swarm intelligence for task allocation in a real time strategy game

Tavares

Zuin²,

Azpúrua³

et al. 2017

JIS

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Real time strategy games are complex scenarios where multiple agents must be coordinated in a dynamic, partially observable environment. In this work, we model coordination as a task allocation problem, in which specific tasks must be properly assigned to agents. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using a genetic algorithm. A fitness estimation method is employed to accelerate execution of the genetic algorithm. To evaluate this approach, we implement this coordination mechanism in the AI of a popular video game: StarCraft: BroodWar. Experiment results show that the genetic algorithm successfully adjusts task allocation parameters. Besides, we assess the trade-off between solution quality and execution time of the genetic algorithm with fitness estimation.

show abstract

Optimizing player behavior in a real-time strategy game using evolutionary algorithms

Cited by 20 publications

References 16 publications

Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

Better GP benchmarks: community survey results and proposals

Combining genetic algorithm and swarm intelligence for task allocation in a real time strategy game

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