Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in sensor networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Focused real-time dynamic programming for MDPs: squeezing more out of a heuristic
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Lazy approximation for solving continuous finite-horizon MDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Solving factored MDPs with hybrid state and action variables
Journal of Artificial Intelligence Research
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Faster heuristic search algorithms for planning with uncertainty and full feedback
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On complexity of multistage stochastic programs
Operations Research Letters
Scalable and convergent multi-robot passive and active sensing
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
On topic selection strategies in multi-agent naming game
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Active Markov information-theoretic path planning for robotic environmental sensing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A cooperative architecture for target localization using multiple AUVs
Intelligent Service Robotics
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The exploration problem is a central issue in mobile robotics. A complete terrain coverage is not practical if the environment is large with only a few small hotspots. This paper presents an adaptive multi-robot exploration strategy that is novel in performing both wide-area coverage and hotspot sampling using non-myopic path planning. As a result, the environmental phenomena can be accurately mapped. It is based on a dynamic programming formulation, which we call the Multi-robot Adaptive Sampling Problem (MASP). A key feature of MASP is in covering the entire adaptivity spectrum, thus allowing strategies of varying adaptivity to be formed and theoretically analyzed in their performance; a more adaptive strategy improves mapping accuracy. We apply MASP to sampling the Gaussian and log-Gaussian processes, and analyze if the resulting strategies are adaptive and maximize wide-area coverage and hotspot sampling. Solving MASP is non-trivial as it comprises continuous state components. So, it is reformulated for convex analysis, which allows discrete-state monotone-bounding approximation to be developed. We provide a theoretical guarantee on the policy quality of the approximate MASP (aMASP) for using in MASP. Although aMASP can be solved exactly, its state size grows exponentially with the number of stages. To alleviate this computational difficulty, anytime algorithms are proposed based on aMASP, one of which can guarantee its policy quality for MASP in real time.