Nguyen Thanh Thien (FPL HCM_K16) (original) (raw)
Papers by Nguyen Thanh Thien (FPL HCM_K16)
adaptive agents and multi-agents systems, Jun 4, 2012
Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coo... more 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 SDCOPs, namely to find a solution with the most stochastically dominating probability distribution reward function; (2) we introduce an algorithm to find such solutions; and (3) we show that stochastically dominating solutions can indeed be less risky than expected reward maximizing solutions.
adaptive agents and multi-agents systems, May 6, 2013
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi... more 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 empirically that our algorithm is able to find solutions that are better than DUCT; and computationally, our algorithm runs faster than DUCT as well as solve some large problems that DUCT failed to solve due to memory limitations.
Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quali... more Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2012
adaptive agents and multi-agents systems, Jun 4, 2012
Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coo... more 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 SDCOPs, namely to find a solution with the most stochastically dominating probability distribution reward function; (2) we introduce an algorithm to find such solutions; and (3) we show that stochastically dominating solutions can indeed be less risky than expected reward maximizing solutions.
adaptive agents and multi-agents systems, May 6, 2013
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi... more 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 empirically that our algorithm is able to find solutions that are better than DUCT; and computationally, our algorithm runs faster than DUCT as well as solve some large problems that DUCT failed to solve due to memory limitations.
Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quali... more Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2012