Planning as search: a quantitative approach
Artificial Intelligence
Introduction to algorithms
Divide and conquer in multi-agent planning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Plan Merging & Plan Reuse as Satisfiability
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
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
A distributed framework for solving the Multiagent Plan Coordination Problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An efficient algorithm for multiagent plan coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Efficient methods for solving the multiagent plan coordination problem
Efficient methods for solving the multiagent plan coordination problem
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Commitment-driven distributed joint policy search
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Anytime coordination using separable bilinear programs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Flaw selection strategies for partial-order planning
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generalizing GraphPlan by formulating planning as a CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Planning for Coordination and Coordination for Planning
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
µ-SATPLAN: Multi-agent planning as satisfiability
Knowledge-Based Systems
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Coordination can be required whenever multiple agents plan to achieve their individual goals independently, but might mutually benefit by coordinating their plans to avoid working at cross purposes or duplicating effort. Although variations of such problems have been studied in the literature, there is as yet no agreement over a general characterization of them. In this paper, we formally define a common coordination problem subclass, which we call the Multiagent Plan Coordination Problem, that is rich enough to represent a wide variety of multiagent coordination problems. We then describe a general framework that extends the partial-order, causal-link plan representation to the multiagent case, and that treats coordination as a form of iterative repair of plan flaws between agents. We show that this algorithmic formulation can scale to the multiagent case better than can a straightforward application of the existing plan coordination techniques, highlighting fundamental differences between our algorithmic framework and these earlier approaches. We then examine whether and how the Multiagent Plan Coordination Problem can be cast as a Distributed Constraint Optimization Problem (DCOP). We do so using ADOPT, a state-of-the-art system that can solve DCOPs in an asynchronous, parallel manner using local communication between individual computational agents. We conclude with a discussion of possible extensions of our work.