Evolutionary approaches to evolve AI scripts for a RTS game

2014 ◽  
Author(s):  
Lucas Ferreira ◽  
Leonardo Pereira ◽  
Claudio Toledo ◽  
Rodrigo Pereira
Keyword(s):  
Rts Game

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

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.

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

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.

Improving heuristic search for RTS-game unit micromanagement using reinforcement learning

2015 ◽  
Author(s):  
Supaphon Kamon ◽  
Tung Due Nguyen ◽  
Tomohiro Harada ◽  
Ruck Thawonmas ◽  
Ikuko Nishikawa
Keyword(s):  
Reinforcement Learning ◽  
Heuristic Search ◽  
Rts Game

Geometrical vs topological measures for the evolution of aesthetic maps in a RTS game

2014 ◽  
Vol 5 (4) ◽  
pp. 251-258 ◽  
Author(s):  
R. Lara-Cabrera ◽  
C. Cotta ◽  
A.J. Fernández-Leiva
Keyword(s):  
Topological Measures ◽  
Rts Game

scholarly journals Design of Emergent and Adaptive Virtual Players in a War RTS Game

2011 ◽  
pp. 372-382 ◽  
Author(s):  
José A. García Gutiérrez ◽  
Carlos Cotta ◽  
Antonio J. Fernández Leiva
Keyword(s):  
Rts Game

scholarly journals Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

2019 ◽  
Author(s):  
Andrew Anderson ◽  
Jonathan Dodge ◽  
Amrita Sadarangani ◽  
Zoe Juozapaitis ◽  
Evan Newman ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Empirical Study ◽  
Mental Models ◽  
Mental Model ◽  
User Study ◽  
Focus Of Attention ◽  
Saliency Maps ◽  
Treatment Experiment ◽  
Rts Game ◽  
The Impact

We present a user study to investigate the impact of explanations on non-experts? understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124 participant, four-treatment experiment to compare participants? mental models of an RL agent in a simple Real-Time Strategy (RTS) game. Our results show that the combination of both saliency and reward bars were needed to achieve a statistically significant improvement in mental model score over the control. In addition, our qualitative analysis of the data reveals a number of effects for further study.

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