Abstract
Real Time Strategy (RTS) games can be very challenging, especially to novice users, who are normally overwhelmed by the dynamic, distributed, and multi-objective structure of these games. In this paper we present RTSMate, an advice system designed to help the player of an RTS game. Using inference mechanisms to reason about the game state and a decision tree to encode its knowledge, RTSMate helps the player by giving him/her tactical and strategical tips about the best actions to be taken according to the current game state, aiming at improving player's performance. This paper describes the main ideas behind the system, its implementation, and the experiments performed using the system in a real game environment. Results show that RTSMate fulfills its objective: most players considered the system useful and were able to improve their performance by using it.
- Alcázar, V., Borrajo, D., and Linares, C. 2008. Modelling a RTS planning domain with Cost Conversion and rewards. In ECAI, A. Botea and C. L. López, Eds. Patras, Greece.Google Scholar
- Baumgarten, R., Colton, S., and Morris, M. 2009. Combining ai methods for learning bots in a Real-Time Strategy Game. International Journal of Computer Games Technology 2009, 10.Google ScholarCross Ref
- Black, A. W. and Taylor, P. A. 1997. The Festival Speech Synthesis System: System documentation. Tech. Rep. HCRC/TR-83, Human Communciation Research Centre, University of Edinburgh, Scotland, UK. Disponível em http://www.cstr.ed.ac.uk/projects/festival.html.Google Scholar
- Blizzard. 2010. WarcraftTM II strategy. http://classic.battle.net/war2/strategy.shtml. Accessed in April 20th, 2010.Google Scholar
- Buro, M. 2002. ORTS: A hack-free RTS game environment. In Proceedings of the International Computers and Games Conference.Google Scholar
- Buro, M. 2004. Call for AI research in RTS games. In Proceedings of the 4th Workshop on Challenges in Game AI. San Jose.Google Scholar
- de Freitas Cunha, R. L. 2010. Um sistema de apoio ao jogador para jogos de estratégia em tempo real. M.S. thesis, Universidade Federal de Minas Gerais.Google Scholar
- Ghuman, D. and Griffiths, M. 2012. A cross-genre study of online gaming: Player demographics, motivation for play, and social interactions among players. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL) 2, 1, 13--29.Google ScholarCross Ref
- Hagelbäck, J. and Johansson, S. J. 2008. Using multi-agent potential fields in real-time strategy games. In AAMAS (2), L. Padgham, D. C. Parkes, J. Müller, and S. Parsons, Eds. IFAAMAS, 631--638. Google ScholarDigital Library
- Hagelbäck, J. and Johansson, S. J. 2009. A multiagent potential field-based bot for real-time strategy games. International Journal of Computer Games Technology 2009, 10.Google ScholarCross Ref
- Lee-Urban, S., Parker, A., Kuter, U., Munoz-Avila, H., and Nau, D. 2007. Transfer learning of hierarchical task-network planning methods in a Real-Time Strategy games. In Proceedings of the Workshop on Artificial Intelligence Planning and Learning.Google Scholar
- McCoy, J. and Mateas, M. 2008. An integrated agent for playing real-time strategy games. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- McGee, K. and Abraham, A. T. 2010. Real-time team-mate AI in Games. In Proceedings of the Fifth International Conference on the Foundations of Digital Games. FDG'10. ACM, New York, NY, USA, 124--131. Google ScholarDigital Library
- Ontañón, S., Mishra, K., Sugandh, N., and Ram, A. 2007. Case-Based planning and execution for Real-Time Strategy games. In ICCBR, R. Weber and M. M. Richter, Eds. Lecture Notes in Computer Science Series, vol. 4626. Springer, 164--178. Google ScholarDigital Library
- Ponsen, M., Munoz-Avila, H., Spronck, P., and Aha, D. W. 2006. Automatically generating game tactics through evolutionary learning. AI Magazine 27, 3, 75--84.Google Scholar
- Ponsen, M. J., Lee-Urban, S., Muñoz-Avila, H., Aha, D. W., and Molineaux, M. 2005. Stratagus: An open-source game engine for research in real-time strategy games. Tech. rep., Navy Center for Naval Research Laboratory.Google Scholar
- Sharma, M., Holmes, M., Santamaria, J., Irani, A., Isbell, C., and Ram, A. 2007. Transfer learning in Real-Time Strategy Games using hybrid CBR/RL. In IJCAI, M. M. Veloso, Ed. Google ScholarDigital Library
- Spronk, P. and Ponsen, M. 2008. Automatic Generation of Strategies. Charles River Media, Hingham, MA, United States, 659--670.Google Scholar
- Sugandh, N., Ontañón, S., and Ram, A. 2008. On-Line Case-Based plan adaptation for Real-Time Strategy games. In AAAI (2008-08-15), D. Fox and C. P. Gomes, Eds. AAAI Press, 702--707. Google ScholarDigital Library
- Weber, B. G. and Mateas, M. 2009. A data mining approach to strategy prediction. In Proceedings of the IEEE Symposium on Computational Intelligence & Games, P. L. Lanzi, Ed. IEEE. Google ScholarDigital Library
- Weber, B. G., Mateas, M., and Jhala, A. 2010. Applying goal-driven autonomy to starcraft. In Proceedings of the Sixth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.Google Scholar
Index Terms
- RTSMate: Towards an Advice System for RTS Games
Recommendations
Knowledge acquisition for adaptive game AI
Game artificial intelligence (AI) controls the decision-making process of computer-controlled opponents in computer games. Adaptive game AI (i.e., game AI that can automatically adapt the behaviour of the computer players to changes in the environment) ...
An Artificial Intelligence System to Help the Player of Real-Time Strategy Games
SBGAMES '10: Proceedings of the 2010 Brazilian Symposium on Games and Digital EntertainmentReal Time Strategy (RTS) games pose a series of challenges to players and AI Agents due to its dynamical, distributed and multi-objective fashion.In this paper, we propose and develop an Artificial Intelligence (AI) system that helps the player during ...
Playing real-time strategy games by imitating human players' micromanagement skills based on spatial analysis
Imitating human micromanagement skills from a massive number of game cases.The influence map was adopted to analyze the influence of units spatially.Imitation learning with a very large number of cases (up to 500,000 cases).Imitation learning responds ...
Comments