Abstract
The response to a large-scale disaster, e.g. an earthquake or a terrorist incident, urges for low-cost policies that coordinate sequential decisions of multiple agents. Decisions range from collective (common good) to individual (self-interested) perspectives, intuitively shaping a two-layer decision model. However, current decision theoretic models are either purely collective or purely individual and seek optimal policies. We present a two-layer, collective versus individual (CvI) decision model and explore the tradeoff between cost reduction and loss of optimality while learning coordination skills. Experiments, in a partially observable domain, test our approach for learning a collective policy and results show near-optimal policies that exhibit coordinated behavior.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abdallah, S., Lesser, V.: Modeling Task Allocation Using a Decision Theoretic Model. In: Fourth Int. Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), pp. 719–726. ACM Press, New York (2005)
Boutilier, C.: Sequential Optimality and Coordination in Multi-Agent Systems. In: Sixteenth Int. Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 478–485 (1999)
Bradtke, S., Duff, M.: Reinforcement learning methods for continuous time Markov decision problems. Advances in Neural Inf. Processing Systems 8, 393–400 (1995)
Corrêa, M., Coelho, H.: Collective Mental States in Extended Mental States Framework. In: International Conference on Collective Intentionality (2004)
Dietterich, T.: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Artificial Intelligence Research 13, 227–303 (2000)
FIPA Communicative Act Library Specification (2002), http://www.fipa.org
Ghavamzadeh, M., Mahadevan, S., Makar, R.: Hierarchical Multi-Agent Reinforcement Learning. Journal of Autonomous Agents and Multi-Agent Systems (2006)
Jonsson, A., Barto, A.: Automated State Abstractions for Options Using the U-Tree Algorithm. Advances in Neural Inf. Processing Systems 13, 1054–1060 (2001)
Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., Shimada, S.: RoboCup Rescue: Search and Rescue in Large-Scale Disasters as a Domain for Autonomous Agents Research. In: Conf. on Man, System and Cyb. (MSC 1999), pp. 739–743 (1999)
Nash, J.: Non-Cooperative Games. Annals of Mathematics 54, 286–295 (1951)
Pynadath, D., Tambe, M.: The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models. Journal of AI Research, 389–423 (2002)
Rohanimanesh, K., Mahadevan, S.: Learning to Take Concurrent Actions. In: Sixteenth Annual Conference on Neural Information Processing Systems, pp. 1619–1626 (2003)
Sutton, R., Precup, D., Singh, S.: Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning. A.I. 112(1-2), 181–211 (1999)
Trigo, P., Coelho, H.: The Multi-Team Formation Precursor of Teamwork. In: Bento, C., Cardoso, A., Dias, G. (eds.) EPIA 2005. LNCS (LNAI), vol. 3808, pp. 560–571. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trigo, P., Jonsson, A., Coelho, H. (2006). Coordination with Collective and Individual Decisions. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_8
Download citation
DOI: https://doi.org/10.1007/11874850_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45462-5
Online ISBN: 978-3-540-45464-9
eBook Packages: Computer ScienceComputer Science (R0)