activity
19982020
most citedResource Allocation Among Agents with MDP-Induced Preferences

44 citations · 104 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI2020

Efficient Querying for Cooperative Probabilistic Commitments

Qi Zhang, Edmund H. Durfee, Satinder Singh

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. Agents whose objectives a…

cs.AI2017

Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making

Qi Zhang, Satinder Singh, Edmund Durfee

In cooperative multiagent planning, it can often be beneficial for an agent to make commitments about aspects of its behavior to others, allowing them in turn to plan their own beh…

cs.MA2014★ 4 cited

Resource-Driven Mission-Phasing Techniques for Constrained Agents in Stochastic Environments

Jianhui Wu, Edmund H. Durfee

Because an agents resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as po…

cs.AI2013★ 17 cited

The Automated Mapping of Plans for Plan Recognition

Marcus J. Huber, Edmund H. Durfee, Michael P. Wellman

To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information…

cs.AI2013★ 20 cited

Plan Development using Local Probabilistic Models

Ella M. Atkins, Edmund H. Durfee, Kang G. Shin

Approximate models of world state transitions are necessary when building plans for complex systems operating in dynamic environments. External event probabilities can depend on st…

cs.MA2011★ 44 cited

Resource Allocation Among Agents with MDP-Induced Preferences

D. A. Dolgov, E. H. Durfee

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actio…