Research Topics

My research interests include, but not limited to the following areas: AI and Games, Noisy Optimisation, Algorithm Portfolio, Reinforcement Learning.

Ph.D thesis My Google Scholar

Publications

2017

Philipp Rohlfshagen, Jialin Liu, Diego Perez-Liebana and Simon M. Lucas, Pac-Man Conquers Academia: Two Decades of Research Using a Classic Arcade Game. [pdf]

Jialin Liu, Julian Togelius, Diego Perez-Liebana and Simon M. Lucas, Evolving Game Skill-Depth using General Video Game AI Agents, 2017 IEEE Congress on Evolutionary Computation (CEC'17'). [pdf]

Jialin Liu, Diego Perez-Liebana and Simon M. Lucas, Bandit-Based Random Mutation Hill-Climbing, 2017 IEEE Congress on Evolutionary Computation (CEC'17'). [pdf]

Kamolwan Kunanusont, Raluca D. Gaina, Jialin Liu, Diego Perez-Liebana and Simon M. Lucas, The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement, 2017 IEEE Congress on Evolutionary Computation (CEC'17'). [pdf]

Raluca D. Gaina, Jialin Liu, Simon M. Lucas, Diego Perez-Liebana, Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing, Proceedings of EvoApplications 2017 (EvoGames). [pdf]

2016

Jialin Liu, Michael Fairbank, Diego Perez and Simon M. Lucas, Optimal Resampling for the Noisy OneMax Problem, Arxiv. [pdf]

Jialin Liu, Diego Perez and Simon M. Lucas, Bandit-based Random Mutation Hill-Climbing, Arxiv. [pdf]

David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu, Fabien Teytaud and Olivier Teytaud, Automatically Reinforcing a Game AI, Arxiv (2016). [pdf]

Jialin Liu, Diego Pérez-Liébana and Simon M. Lucas, Rolling Horizon Coevolutionary Planning for Two-Player Video Games, The Computer science and Electronic Engineering Conference (CEEC) (2016).

Tristan Cazenave, Jialin Liu, Fabien Teytaud, Olivier Teytaud, Learning Opening Books in Partially Observable Games : Using Random Seeds in Phantom Go, 2016 IEEE Computational Intelligence and Games Conference (CIG). [pdf]

Jialin Liu, Oliver Teytaud, Tristan Cazenave, Fast Seed-Learning Algorithms for Games, The 9th International Conference on Computers and Games (CG) (2016). [pdf]

Marie-Liesse Cauwet, Jialin Liu, Baptiste Rozière, Olivier Teytaud, Algorithm Portfolios for Noisy Optimization, Annals of Mathematics and Artificial Intelligence (AMAI), vol. 76, no 1-2, p. 143-172. [pdf] [Springer]

Sandra Astete-Morales, Marie-Liesse Cauwet, Jialin Liu, Olivier Teytaud, Simple and Cumulative Regret for Continuous Noisy Optimization, Theoretical Computer Science (TCS), vol. 617, p. 12-27. [pdf]

2015

Jean-Joseph Christophe, Jérémie Decock, Jialin Liu and Olivier Teytaud, Variance Reduction in Population-Based Optimization : Application to Unit Commitment, Biennial International Conference on Artificial Evolution (EA) (2015). [pdf]

Tristan Cazenave, Jialin Liu, Olivier Teytaud, The Rectangular Seeds of Domineering, Atari-Go and Breakthrough, 2015 IEEE Computational Intelligence and Games Conference (CIG). [pdf]

Mei-Hui Wang, Chi-Shiang Wang, Chang-Shing Lee, Olivier Teytaud, Jialin Liu, Su-Wei Lin and Pi-Hsia Hung, Item Response Theory with Fuzzy Markup Language for Parameter Estimation and Validation, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). [ieeexplore]

Shih-Yuan Chiu, Ching-Nung Lin, Jialin Liu, Tsan-Cheng Su, Fabian Teytaud, Olivier Teytaud and Shi-Jim Yen, Differential Evolution for Strongly Noisy Optimization : Use 1.01^n Resamplings at Iteration n and Reach the −1 Slope, 2015 IEEE Congress on Evolutionary Computation (CEC). [pdf]

David L. St-Pierre, Jialin Liu and Olivier Teytaud, Nash Reweighting of Monte Carlo Simulations : Tsumego, 2015 IEEE Congress on Evolutionary Computation (CEC). [ieeexplore] [slides]

Jérémie Decock, Jialin Liu and Olivier Teytaud, Variance Reduction in Population-Based Optimization: Application to Unit Commitment, Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO). [ACM]

2014

David Auger, Jialin Liu, Sylvie Ruette, David L. St-Pierre and Olivier Teytaud, Sparse Binary Zero-sum Games, the 6th Asian Conference on Machine Learning (ACML) (2014). [pdf]

David L. St-Pierre and Jialin Liu, Differential Evolution Algorithm Applied to Non-stationary Bandit Problem, 2014 IEEE Congress on Evolutionary Computation (CEC). [pdf]

Jialin Liu and Olivier Teytaud, Meta Online Learning: Experiments on a Unit Commitment Problem, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (2014). [pdf]

Jialin Liu, David L. St- Pierre and Olivier Teytaud, A Mathematically Derived Number of Resamplings for Noisy Optimization, Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO) (2014). [pdf]

Marie-Liesse Cauwet, Jialin Liu and Olivier Teytaud, Algorithm Portfolios for Noisy Optimization: Compare Solvers Early, Proceedings of the International Conference on Learning and Intelligent Optimization (LION) (2014). [pdf]

Cheng-Wei Chou, Ping-Chiang Chou, Jean-Joseph Christophe, Adrien Couetoux, Pierre De Freminville, Nicolas Galichet, Chang-Shing Lee, Jialin Liu, David Lupien Saint-Pierre, Michele Sebag, Olivier Teytaud, Mei-Hui Wang, Li-Wen Wu and Shi-Jim Yen, Strategic Choices in Optimization, Journal of Computing and Information Science in Engineering (JCISE), vol. 30, no 3, p. 727-747. [pdf]

2013

Sandra Astete Morales, Jialin Liu and Olivier Teytaud, Log-log Convergence for Noisy Optimization, Biennial International Conference on Artificial Evolution (EA) (2013). [pdf]

Sandra Astete Morales, Jialin Liu and Olivier Teytaud, Noisy Optimization Convergence Rates, Proceedings of the 2013 Annual Conference on Genetic and Evolutionary Computation (GECCO). [pdf]