Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Solution reuse in dynamic constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Frozen development in graph coloring
Theoretical Computer Science - Phase transitions in combinatorial problems
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 Distributed Resource Allocation: A Distributed Constraint Satisfaction Approach
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
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
Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Conflicts in teamwork: hybrids to the rescue
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
The DynCOAA algorithm for dynamic constraint optimization problems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Using Prior Knowledge to Improve Distributed Hill Climbing
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Markets vs auctions: Approaches to distributed combinatorial resource scheduling
Multiagent and Grid Systems - Smart Grid Technologies & Market Models
Airlift mission monitoring and dynamic rescheduling
Engineering Applications of Artificial Intelligence
DynABT: Dynamic Asynchronous Backtracking for Dynamic DisCSPs
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
MaxCAPO: a new expansion of APO to solve distributed constraint satisfaction problems
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
SBDO: A New Robust Approach to Dynamic Distributed Constraint Optimisation
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
Target to sensor allocation: A hierarchical dynamic Distributed Constraint Optimization approach
Computer Communications
Improving the privacy of the asynchronous partial overlay protocol
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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It is now fairly well understood that a vast number of AI problems can be formulated as Constraint Satisfaction Problems (CSPs) and striking improvements have been made in solving them using both centralized and distributed methods. However, many real world problems change over time and very little work has been done in developing methods, particularly distributed ones, for solving problems which exhibit this behavior.This paper presents two new protocols for solving dynamic, distributed constraint satisfaction problems which are based on the classic Distributed Breakout Algorithm (DBA) and the Asynchronous Partial Overlay (APO) algorithm. These two new algorithms are compared on a broad class of problems varying the problems' overall difficulty as well as the rate at which they change over time. The results indicate that neither of the algorithms complete dominates the other on all problem types, but that depending on environmental conditions and the needs of the user, one method may be preferable over the other.