Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
DATMS: a framework for distributed assumption based reasoning
Distributed Artificial Intelligence (Vol. 2)
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
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
Consistency Maintenance for ABT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Constraint Processing
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Variables and constraints in problem domains are often distributed. These distributed constraint satisfaction problems (DCSPs) lend themselves to multiagent solutions. Most existing algorithms for DCSPs are extensions of centralized backtracking or iterative improvement with breakout. Their worst case complexity is exponential. On the other hand, directional consistency based algorithms solve centralized CSPs efficiently if primal graph density is bounded. No known multiagent algorithms solve DCSPs with the same efficiency. We propose the first such algorithm and show that it is sound and complete.