The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Asynchronous Forward-checking for DisCSPs
Constraints
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Increasing tree search efficiency for constraint satisfaction problems
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Learning action models for multi-agent planning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Heuristic multiagent planning with self-interested agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
µ-SATPLAN: Multi-agent planning as satisfiability
Knowledge-Based Systems
Decentralized multi-agent plan repair in dynamic environments
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Generating project plans for data center transformations
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
On the complexity of planning for agent teams and its implications for single agent planning
Artificial Intelligence
Domain-independent multi-agent plan repair
Journal of Network and Computer Applications
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
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We present a fully distributed multi-agent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods to take advantage of the structure of the underlying planning problem, m multi-agent planning domains with limited agent interaction, our algorithm empirically shows scalability beyond state of the art centralized solvers. Our work also provides a novel, real-world setting for testing and evaluating distributed constraint satisfaction algorithms in structured domains and illustrates how existing techniques can be altered to address such structure.