The complexity of Markov decision processes
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The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Selected topics on assignment problems
Discrete Applied Mathematics
Role allocation and reallocation in multiagent teams: towards a practical analysis
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The complexity of multiagent systems: the price of silence
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Communication for Improving Policy Computation in Distributed POMDPs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Real-time hierarchical POMDPs for autonomous robot navigation
Robotics and Autonomous Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive teams of autonomous aerial and ground robots for situational awareness: Field Reports
Journal of Field Robotics - Special Issue on Teamwork in Field Robotics
The permutable POMDP: fast solutions to POMDPs for preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Consensus-based decentralized auctions for robusttask allocation
IEEE Transactions on Robotics
Role-Based Autonomous Multi-robot Exploration
COMPUTATIONWORLD '09 Proceedings of the 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns
Relay Positioning for Unmanned Aerial Vehicle Surveillance*
International Journal of Robotics Research
Motion planning under uncertainty for robotic tasks with long time horizons
International Journal of Robotics Research
Robotics and Autonomous Systems
Efficient planning under uncertainty with macro-actions
Journal of Artificial Intelligence Research
A decision-theoretic characterization of organizational influences
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-robot partially observable Markov decision processes (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Auctions provide a flexible way of coordinating individual policies modeled by POMDPs and have low communication requirements. In addition, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by exploiting a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper presents two different applications: environmental monitoring with unmanned aerial vehicles (UAVs); and cooperative tracking, in which several robots have to jointly track a moving target of interest. The first one is used as a proof of concept and illustrates the proposed ideas through different simulations. The second one adds real multi-robot experiments, showcasing the flexibility and robust coordination that our techniques can provide.