Multi-Variable Agent decomposition for DCOPs

The application of DCOP models to large problems faces two main limitations: (i) Modeling limitations, as each agent can handle only a single variable of the problem; and (ii) Resolution limitations, as current approaches do not exploit the local problem structure within each agent. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decomposition technique, which: (i) Exploits the co-locality of each agent's variables, allowing us to adopt efficient centralized techniques within each agent; (ii) Enables the use of hierarchical parallel models and proposes the use of GPUs; and (iii) Reduces the amount of computation and communication required in several classes of DCOP algorithms.

[1]  Edmund H. Durfee,et al.  Dynamic Prioritization of Complex Agents in Distributed Constraint Satisfaction Problems , 1997, AAAI/IAAI.

[2]  Joël Quinqueton,et al.  Distributed Intelligent Backtracking , 1998, ECAI.

[3]  Victor R. Lesser,et al.  Improved max-sum algorithm for DCOP with n-ary constraints , 2013, AAMAS.

[4]  Steven Okamoto,et al.  Distributed constraint optimization for teams of mobile sensing agents , 2014, Autonomous Agents and Multi-Agent Systems.

[5]  Makoto Yokoo,et al.  Distributed constraint satisfaction algorithm for complex local problems , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[6]  Boi Faltings,et al.  Coordinating Logistics Operations with Privacy Guarantees , 2011, IJCAI.

[7]  Andrea Castelletti,et al.  Multiagent Systems and Distributed Constraint Reasoning for Regulatory Mechanism Design in Water Management , 2015 .

[8]  Makoto Yokoo,et al.  Adopt: asynchronous distributed constraint optimization with quality guarantees , 2005, Artif. Intell..

[9]  Boi Faltings,et al.  A Scalable Method for Multiagent Constraint Optimization , 2005, IJCAI.

[10]  Kenneth N. Brown,et al.  Efficient Handling of Complex Local Problems in Distributed Constraint Optimization , 2006, ECAI.

[11]  Sarvapali D. Ramchurn,et al.  Optimal decentralised dispatch of embedded generation in the smart grid , 2012, AAMAS.

[12]  Enrico Pontelli,et al.  Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems , 2014, CP.

[13]  John Davin,et al.  Hierarchical Variable Ordering for Multiagent Agreement Problems , 2006 .

[14]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[15]  Milind Tambe,et al.  Experimental analysis of privacy loss in DCOP algorithms , 2006, AAMAS '06.

[16]  Evan Sultanik,et al.  On Modeling Multiagent Task Scheduling as a Distributed Constraint Optimization Problem , 2007, IJCAI.

[17]  Milind Tambe,et al.  Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[18]  Hoong Chuin Lau,et al.  Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm , 2013, AAMAS.

[19]  Andrea Castelletti,et al.  Modeling the Management of Water Resources Systems Using Multi-Objective DCOPs , 2015, AAMAS.

[20]  Makoto Yokoo,et al.  Distributed Problem Solving , 2012, AI Mag..

[21]  Makoto Yokoo,et al.  Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems , 2000 .

[22]  Makoto Yokoo,et al.  Distributed Constraint Satisfaction , 2000, Springer Series on Agent Technology.

[23]  Boi Faltings,et al.  PC-DPOP: A New Partial Centralization Algorithm for Distributed Optimization , 2007, IJCAI.

[24]  Meritxell Vinyals,et al.  Divide-and-coordinate: DCOPs by agreement , 2010, AAMAS.

[25]  Amnon Meisels,et al.  Asynchronous Forward Bounding for Distributed COPs , 2014, J. Artif. Intell. Res..

[26]  Milind Tambe,et al.  Analysis of Privacy Loss in Distributed Constraint Optimization , 2006, AAAI.