Using reinforcement learning for city site selection in the turn-based strategy game Civilization IV

Wender, Stefan; Watson, Ian

doi:10.1109/cig.2008.5035664

Cited by 24 publications

(10 citation statements)

References 4 publications

(6 reference statements)

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“…A major problem with real-time strategy games and turn-based strategy games like the Civilization series is the need for micromanagement (Wender and Watson, 2009): in parallel to making high level decisions (where to attack, what kind of technology to research), the player needs to make lower-level decisions, like assigning production and allocate workforce in each city and manage individual soldiers and workers. Micromanagement tasks are tedious and repetitive.…”

Section: Micromanagement and Counsellorsmentioning

confidence: 99%

Reinforcement Learning in Games

Szita

2012

Adaptation, Learning, and Optimization

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Abstract. Reinforcement learning and games have a long and mutually beneficial common history. From one side, games are rich and challenging domains for testing reinforcement learning algorithms. From the other side, in several games the best computer players use reinforcement learning. The chapter begins with a selection of games and notable reinforcement learning implementations. Without any modifications, the basic reinforcement learning algorithms are rarely sufficient for high-level gameplay, so it is essential to discuss the additional ideas, ways of inserting domain knowledge, implementation decisions that are necessary for scaling up. These are reviewed in sufficient detail to understand their potentials and their limitations. The second part of the chapter lists challenges for reinforcement learning in games, together with a review of proposed solution methods. While this listing has a game-centric viewpoint, and some of the items are specific to games (like opponent modelling), a large portion of this overview can provide insight for other kinds of applications, too. In the third part we review how reinforcement learning can be useful in game development and find its way into commercial computer games. Finally, we provide pointers for more in-depth reviews of specific games and solution approaches.

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Section: Micromanagement and Counsellorsmentioning

confidence: 99%

Reinforcement Learning in Games

Szita

2012

Adaptation, Learning, and Optimization

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“…Due to its complexity and multiple possibilities, it has been largely used in game AI research: [7], [26], [31], [41] are some of the authors that have used this platform to validate their theories. Besides Civilization IV we also have other versions and variants of the games of the Civilization series [41]. One of the most popular variants is the open source version of Civilization II called FreeCiv.…”

Section: G Strategy Games (Rts and Tbs)mentioning

confidence: 99%

“…Branavan, Silver and Barzilay have recently used this platform in their research [1]. C-Evo is another open-source variant of Civilization II and, as FreeCiv, is deeply discussed in [41].…”

Section: G Strategy Games (Rts and Tbs)mentioning

confidence: 99%

Player modeling: Towards a common taxonomy

Machado

Fantini

Chaimowicz

2011

2011 16th International Conference on Computer Games (CGAMES)

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Abstract-Artificial Intelligence (AI) is gradually receiving more attention as a fundamental feature to increase the immersion in digital games. Among the several AI approaches, Player Modeling is becoming an important one. The main idea is to try to understand and model the player characteristics and behaviors in order to develop a better AI. This paper presents a survey of the field, discussing the main concepts and proposing a taxonomy to better organize them. We also present several game platforms that can be used by player modeling and AI researchers. We believe that compiling this information can be important to the field, specially to new researchers.

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“…Existing research offers a range of adaptive AI approaches, such as genetic algorithms [1], particle swarm optimization [2], reinforcement learning [3] and neural networks [4], [5], all aimed to create high performing AIs for games. But the problem for AI in game design, as Yannakakis [6] points out, is not to create a good AI, but one that is enjoyable to play against, and one that can be used to improve the game itself.…”

Section: B Related Workmentioning

confidence: 99%

Relevant Information as a formalised approach to evaluate game mechanics

Salge

Mahlmann

2010

Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games

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We present a new approach to use adaptive AI and Information Theory to aid the evaluation of game mechanics. Being able to evaluate the core game mechanics early during production is useful to improve the quality of a game, and ultimately, player satisfaction. A current problem with automated game evaluation via AI is to define measurable parameters that correlate to the quality of the game mechanics. We apply the Information Theory based concept of "Relevant Information" to this problem and argue that there is a relation between enjoyment related game-play properties and Relevant Information for an AI playing the game. We also demonstrate, with a simple game implementation, a.) how an adaptive AI can be used to approximate the Relevant Information, b.) how those measurable numerical values relate to certain game design flaws c.) how this knowledge can be used to improve the game.

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Using reinforcement learning for city site selection in the turn-based strategy game Civilization IV

Cited by 24 publications

References 4 publications

Reinforcement Learning in Games

Reinforcement Learning in Games

Player modeling: Towards a common taxonomy

Relevant Information as a formalised approach to evaluate game mechanics

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