The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Asynchronous Search with Aggregations
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Dynamic prioritization of complex agents in distributed constraint satisfaction problems
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Optimal design in collaborative design network
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
| Hi-index | 0.00 |
When solving Distributed Constraint Satisfaction Problems (DCSP), it is desirable that the search exploits asynchronism as much as possible so that the employed agents can perform much of the work in parallel This allows to utilize the processing power available in a distributed environment However, in many of todays DCSP algorithms, only a few agents are working at any given time and the others are idling This is caused by the fact that once an agent is consistent with its neighbors, it becomes idling until it is forced by other agents to choose a different assignment for its local variables. In this paper we propose a method that utilizes the idling time of the agents to increase the efficiency of a distributed backtracking algorithm where agents have complex local problems and share variables among them An agent computes solutions to its local problem in advance while it is waiting for incoming messages This means that when an agent finds a solution to the local problem that is consistent with higher order agents, it not only informs lower order agents but continuous to search for further solutions which then are stored in a queue When the current local solution becomes invalid due to a nogood received from a lower order agent, the agent does not have to search for a new local solution but can retrieve a precomputed one from the queue This approach increases the amount of work the agents can perform in parallel since higher order agents search ahead for local solutions while lower order agents are trying to expand the current partial solution. Our experiments show that some increase in performance can be gained by queuing local solutions in distributed backtracking.