TY - GEN

T1 - Infinite-horizon proactive dynamic DCOPs

AU - Hoang, Khoi D.

AU - Hou, Ping

AU - Fioretto, Ferdinando

AU - Yeoh, William

AU - Zivan, Roie

AU - Yokoo, Makoto

PY - 2017

Y1 - 2017

N2 - The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-Agent coordination problems. Researchers have recently extended this model to Proactive Dynamic DCOPs (PD-DCOPs) to capture the inherent dynamism present in many coordination problems. The PD-DCOP formulation is a finite-horizon model that assumes a finite horizon is known a priori. It ignores changes to the problem after the horizon and is thus not guaranteed to find optimal solutions for infinite-horizon problems, which often occur in the real world. Therefore, we (i) propose the Infinite-Horizon PD-DCOP (IPD- DCOP) model, which extends PD-DCOPs to handle infinite horizons', (ii) exploit the convergence properties of Markov chains to determine the optimal solution to the problem after it has converged; (Hi) propose three distributed greedy algorithms to solve IPD-DCOPs; (iv) provide theoretical quality guarantees on the new model; and (v) empirically evaluate both proactive and reactive algorithms to determine the tradeoffs between the two classes. The final contribution is important as, thus far. researchers have exclusively evaluated the two classes of algorithms in isolation. As a result, it is difficult to identify the characteristics of problems that they excel in. Our results arc the first in this important direction.

AB - The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-Agent coordination problems. Researchers have recently extended this model to Proactive Dynamic DCOPs (PD-DCOPs) to capture the inherent dynamism present in many coordination problems. The PD-DCOP formulation is a finite-horizon model that assumes a finite horizon is known a priori. It ignores changes to the problem after the horizon and is thus not guaranteed to find optimal solutions for infinite-horizon problems, which often occur in the real world. Therefore, we (i) propose the Infinite-Horizon PD-DCOP (IPD- DCOP) model, which extends PD-DCOPs to handle infinite horizons', (ii) exploit the convergence properties of Markov chains to determine the optimal solution to the problem after it has converged; (Hi) propose three distributed greedy algorithms to solve IPD-DCOPs; (iv) provide theoretical quality guarantees on the new model; and (v) empirically evaluate both proactive and reactive algorithms to determine the tradeoffs between the two classes. The final contribution is important as, thus far. researchers have exclusively evaluated the two classes of algorithms in isolation. As a result, it is difficult to identify the characteristics of problems that they excel in. Our results arc the first in this important direction.

UR - http://www.scopus.com/inward/record.url?scp=85046488134&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046488134&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85046488134

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 212

EP - 220

BT - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017

A2 - Durfee, Edmund

A2 - Das, Sanmay

A2 - Larson, Kate

A2 - Winikoff, Michael

PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

T2 - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017

Y2 - 7 May 2017 through 11 May 2017

ER -