ORCID Profile
0000-0002-5055-3004
Current Organisation
University of Queensland
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Manufacturing robotics | Distributed systems and algorithms | Cyberphysical systems and internet of things | Distributed computing and systems software
Publisher: IEEE
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 16-04-2010
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 2021
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 09-2016
Publisher: The University of Queensland
Date: 31-08-2022
DOI: 10.14264/138E511
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 11-2017
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 06-2019
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: IEEE
Date: 08-2013
Publisher: Elsevier BV
Date: 07-2022
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 08-2014
Publisher: Elsevier BV
Date: 08-2022
Publisher: IEEE
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Elsevier BV
Date: 09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 10-2018
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 12-2017
Publisher: Elsevier BV
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: IEEE
Date: 11-2017
Publisher: Cambridge University Press (CUP)
Date: 12-2011
DOI: 10.1017/S0269888911000178
Abstract: Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear.
Publisher: Oxford University Press (OUP)
Date: 12-03-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: IEEE
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Hindawi Limited
Date: 08-11-2019
DOI: 10.1002/ER.4770
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: MDPI AG
Date: 31-05-2019
DOI: 10.20944/PREPRINTS201901.0202.V2
Abstract: We derive a concentration inequality for the uncertainty in the mean computed by stratified random s ling, and provide an online s ling method based on this inequality.& nbsp Our concentration inequality is versatile and considers a range of factors including: the data ranges, weights, sizes of the strata, the number of s les taken, the estimated s le variances, and whether strata are s led with or without replacement.& nbsp Sequentially choosing s les to minimize this inequality leads to a online method for choosing s les from a stratified population.& nbsp We evaluate and compare the effectiveness of our method against others for synthetic data sets, and also in approximating the Shapley value of cooperative games.& nbsp Results show that our method is competitive with the performance of Neyman s ling with perfect variance information, even without having prior information on strata variances.We also provide a multidimensional extension of our inequality and discuss future applications.
Publisher: IEEE
Date: 11-2017
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2013
DOI: 10.1137/120893501
Publisher: IEEE
Date: 11-2017
Publisher: Oxford University Press (OUP)
Date: 06-08-2014
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 09-2017
Publisher: IEEE
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: Elsevier BV
Date: 12-2020
Publisher: IEEE
Date: 11-2016
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 06-2024
End Date: 06-2029
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded Activity