### Abstract

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.

Original language | English |
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Title of host publication | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 |

Editors | Edmund Durfee, Sanmay Das, Kate Larson, Michael Winikoff |

Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |

Pages | 212-220 |

Number of pages | 9 |

Volume | 1 |

ISBN (Electronic) | 9781510855076 |

Publication status | Published - Jan 1 2017 |

Event | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil Duration: May 8 2017 → May 12 2017 |

### Other

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

Country | Brazil |

City | Sao Paulo |

Period | 5/8/17 → 5/12/17 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence
- Software
- Control and Systems Engineering

### Cite this

*16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017*(Vol. 1, pp. 212-220). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

**Infinite-horizon proactive dynamic DCOPs.** / Hoang, Khoi D.; Hou, Ping; Fioretto, Ferdinando; Yeoh, William; Zivan, Roie; Yokoo, Makoto.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017.*vol. 1, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), pp. 212-220, 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, Sao Paulo, Brazil, 5/8/17.

}

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 - 2017/1/1

Y1 - 2017/1/1

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

VL - 1

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)

ER -