Abstract
We consider in this paper a multi-robot planning system where robots realize a common mission with the following characteristics: the mission is an acyclic graph of tasks with dependencies and temporal window validity. Tasks are distributed among robots which have uncertain durations and resource consumptions to achieve tasks. This class of problems can be solved by using decision-theoretic planning techniques that are able to handle local temporal constraints and dependencies between robots allowing them to synchronize their processing. A specific decision model and a value function allow robots to coordinate their actions at runtime to maximize the overall value of the mission realization. For that, we design in this paper a cooperative multi-robot planning system using distributed Markov Decision Processes (MDPs) without communicating. Robots take uncertainty on temporal intervals and dependencies into consideration and use a distributed value function to coordinate the actions of robots.
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
J. Bresina, R. Dearden, N. Meuleau, S. Ramakrishnan, D. Smith, and R. Washington. Planning under continuous time and resource uncertainty: A challenge for ai. In UAI, 2002.
C. Bererton, G. Gordon, and S. Thrun. Auction mechanism design for multi-robot coordination. In S. Thrun, L. Saul, and B. Schölkopf, editors, Proceedings of Conference on Neural Information Processing Systems (NIPS). MIT Press, 2003.
Graig Boutilier. Sequential optimality and coordination in multiagents systems. In IJCAI, 1999.
D. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of mdps. In UAI, 2000.
R. Becker, S. Zilberstein, V. Lesser, and C. Goldman. Transitionindependent decentralized markov decision processes. In AAMAS, 2003.
S. Cardon, AI. Mouaddib, S. Zilberstein, and R. Washington. Adaptive control of acyclic progressive processing task structures. In IJCAI, pages 701–706, 2001.
C. Guestrin, D. Koller, and R. Parr. Multiagent planning with factored mdps. In NIPS, 2001.
C. Goldman and S. Zilberstein. Optimizing information exchange in cooperative multiagent systems. In AAMAS, 2003.
H. Hanna and AI Mouaddib. Task selection as decision making in multiagent system. In AAMAS, pages 616–623, 2002.
R. Nair, D. Pynadath, M. Yokoo, M. Tambe, and S. Marsella. Taming decentralized pomdps: Towards efficient policy computation for multiagent settings. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 2003.
L. Peshkin, K.E. Kim, N. Meuleu, and L.P. Kaelbling. Learning to cooperate via policy search. In UAI, pages 489–496, 2000.
R.S. Sutton and A.G. Barto. Reinforcement learning: An introduction. MIT press, Cambrige, MA, 1998.
P. Xuan, V. Lesser, and S. Zilberstein. Communication decisions in multiagent cooperation. In Autonomous Agents, pages 616–623, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer
About this paper
Cite this paper
Beynier, A., Mouaddib, AI. (2007). Decentralized Markov Decision Processes for Handling Temporal and Resource constraints in a Multiple Robot System. In: Alami, R., Chatila, R., Asama, H. (eds) Distributed Autonomous Robotic Systems 6. Springer, Tokyo. https://doi.org/10.1007/978-4-431-35873-2_19
Download citation
DOI: https://doi.org/10.1007/978-4-431-35873-2_19
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-35869-5
Online ISBN: 978-4-431-35873-2
eBook Packages: EngineeringEngineering (R0)