Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Computationally Manageable Combinational Auctions
Management Science
Stochastic dynamic programming with factored representations
Artificial Intelligence
Computing Factored Value Functions for Policies in Structured MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Greedy linear value-approximation for factored Markov decision processes
Eighteenth national conference on Artificial intelligence
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Planning under uncertainty in complex structured environments
Planning under uncertainty in complex structured environments
Graphical Models in Local, Asymmetric Multi-Agent Markov Decision Processes
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
Mechanism design and deliberative agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Computationally-efficient combinatorial auctions for resource allocation in weakly-coupled MDPs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Combinatorial resource scheduling for multiagent MDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Resource allocation among agents with MDP-induced preferences
Journal of Artificial Intelligence Research
Token Based Resource Sharing in Heterogeneous Multi-agent Teams
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
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Distributing scarce resources among agents in a way that maximizes the social welfare of the group is a computationally hard problem when the value of a resource bundle is not linearly decomposable. Furthermore, the problem of determining the value of a resource bundle can be a significant computational challenge in itself, such as for an agent operating in a stochastic environment, where the value of a resource bundle is the expected payoff of the optimal policy realizable given these resources. Recent work has shown that the structure in agents' preferences induced by stochastic policy-optimization problems (modeled as MDPs) can be exploited to solve the resource-allocation and the policy-optimization problems simultaneously, leading to drastic (often exponential) improvements in computational efficiency. However, previous work used a flat MDP model that scales very poorly. In this work, we present and empirically evaluate a resource-allocation mechanism that achieves much better scaling by using factored MDP models, thus exploiting both the structure in agents' MDP-induced preferences, as well as the structure within agents' MDPs.