ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Interaction-driven Markov games for decentralized multiagent planning under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Decentralized MDPs with sparse interactions
Artificial Intelligence
Collective Decision-Theoretic Planning for Planet Exploration
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Coordinated multi-robot exploration
IEEE Transactions on Robotics
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In this paper, we propose an approach based on an interaction-oriented resolution of decentralized Markov decision processes (Dec-MDPs) primary motivated by a real-world application of decentralized decision makers to explore and map an unknown environment. This interaction-oriented resolution is based on distributed value functions (DVF) techniques that decouple the multi-agent problem into a set of individual agent problems and consider possible interactions among agents as a separate layer. This leads to a significant reduction of the computational complexity by solving Dec-MDPs as a collection of MDPs. Using this model in multi-robot exploration scenarios, we show that each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team. Our technique has been implemented and evaluated in simulation and in real-world scenarios during a robotic challenge for the exploration and mapping of an unknown environment by mobile robots. Experimental results from real-world scenarios and from the challenge are given where our system was vice-champion.