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Using multi-agent potential fields in real-time strategy games

Published: 12 May 2008 Publication History

Abstract

Bots for Real Time Strategy (Rts) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a Multi-agent Potential Field based bot architecture that is evaluated in a real time strategy game setting and compare it, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. Although our solution did not reach the performance standards of traditional Rts bots in the test, we see great unexploited benefits in using multi-agent potential field based solutions in Rts games.

References

[1]
R. C. Arkin. Motor schema based navigation for a mobile robot. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 264--271, 1987.
[2]
J. Borenstein and Y. Koren. Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, 19:1179--1187, 1989.
[3]
J. Borenstein and Y. Koren. The vector field histogram: fast obstacle avoidance for mobile robots. IEEE Journal of Robotics and Automation, 7(3):278--288, 1991.
[4]
M. Buro. ORTS --- A Free Software RTS Game Engine, 2007. http://www.cs.ualberta.ca/ mburo/orts/ URL last visited on 2008-01-25.
[5]
A. Howard, M. Matarić, and G. Sukhatme. Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Proceedings of the 6th International Symposium on Distributed Autonomous Robotics Systems (DARS02), 2002.
[6]
S. Johansson. On using multi-agent systems in playing board games. In Proceedings of Autonomous Agents and Multi-agent Systems (Aamas), 2006.
[7]
S. Johansson and A. Saffiotti. An electric field approach to autonomous robot control. In RoboCup 2001, number 2752 in Lecture notes in artificial intelligence. Springer Verlag, 2002.
[8]
O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1):90--98, 1986.
[9]
O. Khatib. Human-like motion from physiologically-based potential energies. In J. Lenarcic and C. Galletti, editors, On Advances in Robot Kinematics, pages 149--163. Kluwer Academic Publishers, 2004.
[10]
S. Kraus and D. Lehmann. Designing and building a negotiating automated agent. Computational Intelligence, 11(1):132--171, 1995.
[11]
M. Mamei and F. Zambonelli. Motion coordination in the quake 3 arena environment: A field-based approach. In Proceedings of Autonomous Agents and Multi-Agent Systems (AAMAS). IEEE Computer Society, 2004.
[12]
M. Massari, G. Giardini, and F. Bernelli-Zazzera. Autonomous navigation system for planetary exploration rover based on artificial potential fields. In Proceedings of Dynamics and Control of Systems and Structures in Space (DCSSS) 6th Conference, 2004.
[13]
C. Niederberger and M. H. Gross. Towards a game agent. Technical Report 377, Swiss Federal Institute of Technology, Zürich, 2003.
[14]
T. Röfer, R. Brunn, I. Dahm, M. Hebbel, J. Homann, M. Jüngel, T. Laue, M. Lötzsch, W. Nistico, and M. Spranger. GermanTeam 2004 - the german national Robocup team, 2004.
[15]
C. Thurau, C. Bauckhage, and G. Sagerer. Learning human-like movement behavior for computer games. In Proc. 8th Int. Conf. on the Simulation of Adaptive Behavior (SAB'04), 2004.
[16]
S. L. Tomlinson. The long and short of steering in games. International Journal of Simulations, 1-2(5), 2004.
[17]
P. Vadakkepat, K. Chen Tan, and W. Ming-Liang. Evolutionary artificial potential fields and their application in real time robot path planning. In Proceedings of the 2000 Congress on Evolutionary Computation, pages 256--263. IEEE Press, 2000.
[18]
M. van Lent, J. Laird, J. Buckman, J. Hartford, S. Houchard, K. Steinkraus, and R. Tedrake. Intelligent agents in computer games. In Proceedings of AAAI. AAAI, 1999.

Cited By

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  • (2018)The facets of artificial intelligenceProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304730(5180-5187)Online publication date: 13-Jul-2018
  • (2018)Real-time strategy game micro for tactical training simulationsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208288(1656-1663)Online publication date: 6-Jul-2018
  • (2015)RTSMateComputers in Entertainment10.1145/2582193.263344112:1(1-20)Online publication date: 10-Feb-2015
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Published In

cover image ACM Conferences
AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
May 2008
673 pages
ISBN:9780981738116

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 12 May 2008

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Author Tags

  1. Orts
  2. Rts games
  3. artificial potential fields
  4. multi-agent bot

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  • Research-article

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AAMAS08
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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

View all
  • (2018)The facets of artificial intelligenceProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304730(5180-5187)Online publication date: 13-Jul-2018
  • (2018)Real-time strategy game micro for tactical training simulationsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208288(1656-1663)Online publication date: 6-Jul-2018
  • (2015)RTSMateComputers in Entertainment10.1145/2582193.263344112:1(1-20)Online publication date: 10-Feb-2015
  • (2012)A novel agent based control scheme for RTS gamesProceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System10.1145/2336727.2336734(1-9)Online publication date: 21-Jul-2012
  • (2010)ReviewEnvironmental Modelling & Software10.1016/j.envsoft.2010.04.02125:12(1490-1507)Online publication date: 1-Dec-2010
  • (2010)Evolving behaviour trees for the commercial game DEFCONProceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I10.1007/978-3-642-12239-2_11(100-110)Online publication date: 7-Apr-2010
  • (2009)Agent-oriented control in real-time computer gamesProceedings of the 7th international conference on Programming multi-agent systems10.5555/1928304.1928327(266-283)Online publication date: 10-May-2009
  • (2009)Multi-agent navigation using path-based vector fieldsProceedings of the 7th German conference on Multiagent system technologies10.5555/1791994.1791998(4-15)Online publication date: 9-Sep-2009
  • (2009)Measuring player experience on runtime dynamic difficulty scaling in an RTS gameProceedings of the 5th international conference on Computational Intelligence and Games10.5555/1719293.1719311(46-52)Online publication date: 7-Sep-2009
  • (2008)Demonstration of multi-agent potential fields in real-time strategy gamesProceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers10.5555/1402744.1402766(1687-1688)Online publication date: 12-May-2008

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