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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau
Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this paper, we introduce a new sampling-based DCOP algorithm called Distributed Gibbs, whose memory requirements per agent is linear in the number of agents in the problem. Additionally, we show …
Stochastic Dominance In Stochastic Dcops For Risk-Sensitive Applications, Nguyen Duc Thien, William Yeoh, Hoong Chuin Lau
Stochastic Dominance In Stochastic Dcops For Risk-Sensitive Applications, Nguyen Duc Thien, William Yeoh, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known probability distribution reward functions, and introduced algorithms to find solutions with the largest expected reward. Unfortunately, such a solution might be very risky, that is, very likely to result in a poor reward. Thus, in this paper, we make three contributions: (1) we propose a stricter objective for …