Distributed Algorithms
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Taking advantage of stable sets of variables in constraint satisfaction problems
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Using relaxations to improve search in distributed constraint optimisation
Artificial Intelligence Review
ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking
Autonomous Agents and Multi-Agent Systems
On modeling multiagent task scheduling as a distributed constraint optimization problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Analyzing the performance of distributed algorithms
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Dynamic configuration of agent organizations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
| Hi-index | 0.00 |
Many different multi-agent problems, such as distributed scheduling, can be formalized as distributed constraint optimization problems (DCOP [1]). Ordering the constraint variables is an important preprocessing step of the ADOPT algorithm [1], the state of the art method of solving DCOP. Currently ADOPT uses depth-first search (DFS) trees for that purpose. For certain classes of tasks DFS ordering does not exploit the problem structure as compared to pseudo-tree ordering [3]. Also the variables are currently ordered by using a centralized scheme, which requires global information about the problem structure.We present a variable ordering algorithm, which is both decentralized and makes use of pseudo-trees, thus exploiting the problem structure when possible. This allows to apply ADOPT to domains, where global information is unavailable, and find solutions more efficiently. The worst-case pseudo-tree depth resulting from our algorithm is √2k|V|, where V is the set of variables, and k is maximum cluster size in constraint graph. The algorithm has space and time complexity polynomial in size of the constraint graph.