@conference {1508853,
	title = {On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints },
	booktitle = {Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
	year = {2007},
	abstract = {Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve. In this paper,
we improve a state-of-the-art heuristic solution method for DECMDPs, called OC-DEC-MDP, that has recently been shown to scale
up to larger DEC-MDPs. Our heuristic solution method, called
Value Function Propagation (VFP), combines two orthogonal improvements of OC-DEC-MDP. First, it speeds up OC-DEC-MDP
by an order of magnitude by maintaining and manipulating a value
function for each state (as a function of time) rather than a separate value for each pair of state and time interval. Furthermore, it
achieves better solution qualities than OC-DEC-MDP because, as
our analytical results show, it does not overestimate the expected
total reward like OC-DEC- MDP. We test both improvements independently in a crisis-management domain as well as for other
types of domains. Our experimental results demonstrate a significant speedup of VFP over OC-DEC-MDP as well as higher solution
qualities in a variety of situations.},
	author = {Janusz Marecki and Tambe, Milind}
}
