Encodings of non-binary constraint satisfaction problems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Introduction to Linear Optimization
Introduction to Linear Optimization
Inference-Based Sensitivity Analysis for Mixed Integer/Linear Programming
Operations Research
Multiagent planning for agents with internal execution resource constraints
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Sensitivity Analysis for Scheduling Problems
Journal of Scheduling
The Knowledge Engineering Review
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Towards a Formalization of Teamwork with Resource Constraints
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
On k-optimal distributed constraint optimization algorithms: new bounds and algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Resource constrained distributed constraint optimization with virtual variables
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
PC-DPOP: a new partial centralization algorithm for distributed optimization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hi-index | 0.00 |
Previous work in multiagent coordination has addressed the challenge of planning in domains where agents must optimize a global goal, while satisfying local resource constraints. However, the imposition of resource constraints naturally raises the question of whether the agents could significantly improve their team performance if a few more resources were made available. Sensitivity analysis aims to answer that question. This paper focuses on sensitivity analysis in the context of the distributed coordination framework, Multiply-Constrained DCOP (MC-DCOP). There are three main challenges in performing sensitivity analysis: (i) to perform it in a distributed fashion, (ii) to avoid re-solving an NP-hard MC-DCOP optimization from scratch, and (iii) to avoid considering unproductive uses for extra resources. To meet these challenges, this paper presents three types of locally optimal algorithms: link analysis, local reoptimization and local constraint propagation. These algorithms are distributed and avoid redundant computation by ascertaining just the effects of local perturbations on the original problem. Deploying our algorithms on a large number of MC-DCOP problems revealed several results. While our cheapest algorithm successfully identified quality improvements for a few problems, our more complex techniques were necessary to identify the best uses for additional resources. Furthermore, we identified two heuristics that can help identify a priori which agents might benefit most from additional resources: density rank, which works well when nodes received identical resources and remaining resource rank, which works well when nodes received resources based on the number of neighbors they had.