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Khoi D. Hoang

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3 papers

1 author row

JAIR Journal 2022 Journal Article

Proactive Dynamic Distributed Constraint Optimization Problems

Khoi D. Hoang
Ferdinando Fioretto
Ping Hou
William Yeoh
Makoto Yokoo
Roie Zivan

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-agent coordination problems. To solve DCOPs in a dynamic environment, Dynamic DCOPs (D-DCOPs) have been proposed to model the inherent dynamism present in many coordination problems. D-DCOPs solve a sequence of static problems by reacting to changes in the environment as the agents observe them. Such reactive approaches ignore knowledge about future changes of the problem. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model D-DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model possible changes of the problem and take such information into account when solving the dynamically changing problem in a proactive manner. The additional expressivity of this formalism allows it to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models: (i) We introduce Proactive Dynamic DCOPs (PD-DCOPs), which explicitly model how the DCOP will change over time; (ii) We develop exact and heuristic algorithms to solve PD-DCOPs in a proactive manner; (iii) We provide theoretical results about the complexity of this new class of DCOPs; and (iv) We empirically evaluate both proactive and reactive algorithms to determine the trade-offs between the two classes. The final contribution is important as our results are the first that identify the characteristics of the problems that the two classes of algorithms excel in.

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AAMAS Conference 2017 Conference Paper

Infinite-Horizon Proactive Dynamic DCOPs

Khoi D. Hoang
Ping Hou
Ferdinando Fioretto
William Yeoh
Roie Zivan
Makoto Yokoo

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; (iii) 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 are the first in this important direction.

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AAMAS Conference 2016 Conference Paper

Proactive Dynamic Distributed Constraint Optimization

Khoi D. Hoang
Ferdinando Fioretto
Ping Hou
Makoto Yokoo
William Yeoh
Roie Zivan

Current approaches that model dynamism in DCOPs solve a sequence of static problems, reacting to changes in the environment as the agents observe them. Such approaches thus ignore possible predictions on future changes. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem. The additional expressivity of this formalism allows it to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models: (i) we introduce the PD-DCOP model, which explicitly captures dynamic changes of the DCOP over time; (ii) we discuss the complexity of this new class of DCOPs; and (iii) we develop both exact and approximation algorithms with quality guarantees to solve PD- DCOPs proactively.

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