Merging BSP trees yields polyhedral set operations
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Point-based dynamic programming for DEC-POMDPs
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 transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
A fast analytical algorithm for solving Markov decision processes with real-valued resources
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning with continuous resources in stochastic domains
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Continuous time planning for multiagent teams with temporal constraints
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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We present an approximation method that solves a class of Decentralized hybrid Markov Decision Processes (DEC-HMDPs). These DEC-HMDPs have both discrete and continuous state variables and represent individual agents with continuous measurable state-space, such as resources. Adding to the natural complexity of decentralized problems, continuous state variables lead to a blowup in potential decision points. Representing value functions as Rectangular Piecewise Constant (RPWC) functions, we formalize and detail an extension to the Coverage Set Algorithm (CSA) [1] that solves transition independent DEC-HMDPs with controlled error. We apply our algorithm to a range of multi-robot exploration problems with continuous resource constraints.