Discovering Meaningful Labelings for RTS Game Replays via Replay Embeddings

BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7197 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13849-13850

Author(s):

Donghyeon Lee ◽

Man-Je Kim ◽

Chang Wook Ahn

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Real Time ◽

Large Scale ◽

Outcome Predictor ◽

Short Term ◽

Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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Integrated Balancing of an RTS Game: Case Study and Toolbox Refinement

2018 IEEE Conference on Computational Intelligence and Games (CIG) ◽

10.1109/cig.2018.8490426 ◽

2018 ◽

Cited By ~ 3

Author(s):

Mike Preuss ◽

Thomas Pfeiffer ◽

Vanessa Volz ◽

Nicolas Pflanzl

Keyword(s):

Rts Game

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An Analysis of the Effects of Physical Abilities on RTS Game Outcome Using Machine Learning Approach

2019 International Conference on Information and Communication Technology Convergence (ICTC) ◽

10.1109/ictc46691.2019.8939771 ◽

2019 ◽

Author(s):

Dae-Wook Kim ◽

Sungyun Park ◽

Seong-il Yang

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Game Outcome ◽

Physical Abilities ◽

Rts Game

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Determining Solution Space Characteristics for Real-Time Strategy Games and Characterizing Winning Strategies

International Journal of Computer Games Technology ◽

10.1155/2011/834026 ◽

2011 ◽

Vol 2011 ◽

pp. 1-17

Author(s):

Kurt Weissgerber ◽

Gary B. Lamont ◽

Brett J. Borghetti ◽

Gilbert L. Peterson

Keyword(s):

Real Time ◽

A Priori ◽

Solution Space ◽

Complex Problem ◽

Classification Model ◽

Landscape Characteristics ◽

Specific Complex ◽

Winning Strategies ◽

Strategic Agents ◽

Rts Game

The underlying goal of a competing agent in a discrete real-time strategy (RTS) game is to defeat an adversary. Strategic agents or participants must define an a priori plan to maneuver their resources in order to destroy the adversary and the adversary's resources as well as secure physical regions of the environment. This a priori plan can be generated by leveraging collected historical knowledge about the environment. This knowledge is then employed in the generation of a classification model for real-time decision-making in the RTS domain. The best way to generate a classification model for a complex problem domain depends on the characteristics of the solution space. An experimental method to determine solution space (search landscape) characteristics is through analysis of historical algorithm performance for solving the specific problem. We select a deterministic search technique and a stochastic search method for a priori classification model generation. These approaches are designed, implemented, and tested for a specific complex RTS game, Bos Wars. Their performance allows us to draw various conclusions about applying a competing agent in complex search landscapes associated with RTS games.

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