Solution reuse in dynamic constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
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
Comparing two approaches to dynamic, distributed constraint satisfaction
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Journal of Artificial Intelligence Research
Asynchronous backtracking without adding links: a new member in the ABT family
Artificial Intelligence - Special issue: Distributed constraint satisfaction
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Constraint Satisfaction has been widely used to model static combinatorial problems. However, many AI problems are dynamic and take place in a distributed environment, i.e. the problems are distributed over a number of agents and change over time. Dynamic Distributed Constraint Satisfaction Problems (DDisCSP) [1] are an emerging field for the resolution of problems that are dynamic and distributed in nature. In this paper, we propose DynABT, a new Asynchronous algorithm for DDisCSPs which combines solution and reasoning reuse i.e. it handles problem changes by modifying the existing solution while re-using knowledge gained from solving the original (unchanged) problem. The benefits obtained from this approach are two-fold: (i) new solutions are obtained at a lesser cost and; (ii) resulting solutions are stable i.e. close to previous solutions. DynABT has been empirically evaluated on problems of varying difficulty and several degrees of changes has been found to be competitive for the problem classes tested.