Abstract
This paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in real-time.
The performance of the task allocation mechanism is evaluated and compared in different implementation variants, and results show that the proposed MRTA system significantly increases the team performance, when compared to pre-programmed team behavior algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bianchi, R.A.C., Ribeiro, C., Costa, A.: Accelerating autonomous learning by using heuristic selection of actions. Journal of Heuristics 14, 135–168 (2008)
Browning, B., Bruce, J., Bowling, M., Veloso, M.: STP: Skills, tactics and plays for multi-robot control. IEEE Journal of Control and Systems Engineering 219, 33–52 (2005)
Bruce, J., Zickler, S., Licitra, M., Veloso, M.: Cmdragons: Dynamic passing and strategy on a champion robot soccer team. In: Proceedings of the IEEE Int. Conf. on Robotics and Automation (ICRA), Pasadena, CA (2008)
Celiberto Jr., L.A., Ribeiro, C.H.C., Costa, A.H.R., Bianchi, R.A.C.: Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.) RoboCup 2007: Robot Soccer World Cup XI. LNCS (LNAI), vol. 5001, pp. 220–227. Springer, Heidelberg (2008)
Dias, M.B., Zlot, R.M., Zinck, M.B., Gonzalez, J.P., Stentz, A.T.: A versatile implementation of the traderbots approach for multirobot coordination. In: Int. Conf. on Intelligent Autonomous Systems (2004)
Dias, M., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE 94(7), 1257–1270 (2006)
Gerkey, B., Matarić, M.: Sold!: auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation 18(5), 758–768 (2002)
Gerkey, B.P., Matarić, M.J.: Multi-robot task allocation: analyzing the complexity and optimality of key architectures. In: Proceedings of IEEE Int. Conf. on Robotics and Automation, ICRA 2003, vol. 3, pp. 3862–3868 (September 2003)
Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. Journal of Robotics Research 23(9), 939–954 (2004)
Kose, H., Tatlidede, U., Mericli, C., Kaplan, K., Akin, H.L.: Q-learning based market-driven multi-agent collaboration in robot soccer. In: Proceedings of the Turkish Symposium on Artificial Intelligence and Neural Networks, pp. 219–2228 (2004)
Kyrylov, V.: Balancing Gains, Risks, Costs, and Real-Time Constraints in the Ball Passing Algorithm for the Robotic Soccer. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006: Robot Soccer World Cup X. LNCS (LNAI), vol. 4434, pp. 304–313. Springer, Heidelberg (2007)
Parker, L.E., Tang, F.: Building multirobot coalitions through automated task solution synthesis. Proceedings of the IEEE 94(7), 1289–1305 (2006)
Parker, L.E.: Distributed intelligence: Overview of the field and its application in multi-robot systems. Journal of Physical Agents 2(1), 5–14 (2008); special issue on Multi-Robot Systems
Sandholm, T., Suri, S.: Improved algorithms for optimal winner determination in combinatorial auctions and generalizations. In: Proceedings of the Seventeenth National Conf. on Artificial Intelligence, pp. 90–97 (2000)
Stone, P., Sutton, R.S., Kuhlmann, G.: Reinforcement learning for RoboCup-soccer keepaway. Adaptive Behavior 13(3), 165–188 (2005)
Sukthankar, G., Sycara, K.: Robust recognition of physical team behaviors using spatio-temporal models. In: AAMAS 2006: Proceedings of the Fifth Int. Joint Conf. on Autonomous Agents and Multiagent Systems, pp. 638–645. ACM (2006)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Tang, F., Parker, L.E.: A complete methodology for generating multi-robot task solutions using asymtre-d and market-based task allocation. In: 2007 IEEE Int. Conf. on Robotics and Automation, pp. 3351–3358 (April 2007)
Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research 10(1), 1633–1685 (2009)
Vail, D., Veloso, M.: Feature selection for activity recognition in multi-robot domains. In: AAAI 2008, Twenty-third Conf. on Artificial Intelligence (2008)
Watkins, C.J.C.H.: Learning from Delayed Rewards. Ph.D. thesis, University of Cambridge (1989)
Weigel, T., Auerbach, W., Dietl, M., Dümler, B., Gutmann, J.-S., Marko, K., Müller, K., Nebel, B., Szerbakowski, B., Thiel, M.: CS Freiburg: Doing the Right Thing in a Group. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, p. 52. Springer, Heidelberg (2001)
Werger, B., Mataric, M.J.: Broadcast of local eligibility for multi-target observation. In: 5th Int. Symposium on Distributed Autonomous Robotic Systems (DARS), pp. 347–356 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gurzoni, J.A., Tonidandel, F., Bianchi, R.A.C. (2011). Market-Based Dynamic Task Allocation Using Heuristically Accelerated Reinforcement Learning. In: Antunes, L., Pinto, H.S. (eds) Progress in Artificial Intelligence. EPIA 2011. Lecture Notes in Computer Science(), vol 7026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24769-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-24769-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24768-2
Online ISBN: 978-3-642-24769-9
eBook Packages: Computer ScienceComputer Science (R0)