no code implementations • 8 May 2024 • Paul Mingzheng Tang, Kenji Kah Hoe Leong, Nowshad Shaik, Hoong Chuin Lau
In this paper, we explore the potential application of Large Language Models (LLMs) that will automatically model constraints and generate code for dynamic scheduling problems given an existing static model.
no code implementations • 31 Aug 2023 • Paul Mingzheng Tang, Ba Phong Tran, Hoong Chuin Lau
In this paper, we are concerned with the automated exchange of orders between logistics companies in a marketplace platform to optimize total revenues.
no code implementations • 7 Dec 2022 • Ambrogio Maria Bernardelli, Stefano Gualandi, Hoong Chuin Lau, Simone Milanesi
While the previous approaches achieve an average accuracy of 51. 1% on the MNIST dataset, the BeMi ensemble achieves an average accuracy of 61. 7% when trained with 10 images per class and 76. 4% when trained with 40 images per class.
1 code implementation • 1 Nov 2022 • Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau, Zaharah Bukhsh
To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search.
no code implementations • 19 Mar 2021 • Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan, Tao Luo, Qianxiao Li, Hoong Chuin Lau
In this way, we are able capture the common structure of the instances and their interactions with the solver, and produce good choices of penalty parameters with fewer number of calls to the QUBO solver.
no code implementations • NeurIPS 2018 • Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment.
no code implementations • NeurIPS 2017 • Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system.
no code implementations • 27 Mar 2018 • Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau
A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i. e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents.
no code implementations • 27 Aug 2017 • Truc Viet Le, Richard J. Oentaryo, Siyuan Liu, Hoong Chuin Lau
In this work, we address their efficiency issues by proposing local GPs to learn from and make predictions for correlated subsets of data.
no code implementations • 18 Jan 2014 • Na Fu, Hoong Chuin Lau, Pradeep R. Varakantham, Fei Xiao
Thus, in this paper, our focus is on providing a scalable method for solving RCPSP/max problems with durational uncertainty.