Research

Research Interests

My research interests include, but not limited to the following areas: game AI (AI for playing games, AI for designing games), noisy optimisation (derivation-free optimisation, dynamic resampling strategies), algorithm portfolio.

My Google Scholar

Publications

2019

Hao Tong, Changwu Huang, Jialin Liu and Xin Yao, Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems, ArXiv, 2019. [pdf]

Simon M. Lucas, Jialin Liu, Ivan Bravi, Raluca D. Gaina, John Woodward, Vanessa Volz and Diego Perez-Liebana, Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is the Best, AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence, 2019. [pdf]

2018

Chiara Sironi, Jialin Liu and Mark Winands, Self-Adaptive Monte-Carlo Tree Search in General Game Playing, IEEE Transactions on Games, 2018. [ieeexplore] (SCI)

Chang-Shing Lee, Mei-Hui Wang, Chi-Shiang Wang, Olivier Teytaud, Jialin Liu, Su-Wei Lin, Pi-Hsia Hung, PSO-based fuzzy markup language for student learning performance evaluation and educational application, IEEE Transactions on Fuzzy Systems (2018). [pdf] (SCI)

Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa, Raluca D. Gaina, Julian Togelius, Simon M. Lucas, General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms. [pdf]

Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M Lucas, Adam Smith, Sebastian Risi, Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network, Proceedings of the 2018 Annual Conference on Genetic and Evolutionary Computation (GECCO). [pdf] (Best Paper Award, DETA+THEORY+GECH tracks) (EI)

Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu and Diego Perez Liebana, Deep reinforcement learning in the General Video Game AI framework, 2018 IEEE Computational Intelligence and Games Conference (CIG). [pdf] (EI)

Ivan Bravi, Diego Perez, Simon Lucas and Jialin Liu, Shallow decision-making analysis in General Video Game Playing, 2018 IEEE Computational Intelligence and Games Conference (CIG). [pdf] (EI)

Chiara F. Sironi, Jialin Liu, Diego Perez-Liebana, Raluca D. Gaina, Ivan Bravi, Simon M. Lucas, Mark H.M. Winands, Self-Adaptive MCTS for General Video Game Playing, Applications of Evolutionary Computation: 21st European Conference, EvoApplications 2018. [pdf] (EI)

Simon M. Lucas, Jialin Liu, Diego Perez-Liebana, The n-tuple bandit evolutionary algorithm for game agent optimisation, 2018 IEEE Congress on Evolutionary Computation (CEC'18). [pdf] (shortlisted for Best Paper Award from 347 accepted papers) (EI)

Marie-Liesse Cauwet, Jeremie Decock, Jialin Liu, Olivier Teytaud, Direct Model Predictive Control: A Theoretical and Numerical Analysis, 20th Power Systems Computation Conference (PSCC 2018). [pdf] (EI)

Vanessa Volz, Dan Ashlock, Simon Colton, Steve Dahlskog, Jialin Liu, Simon M. Lucas, Diego Perez Liebana, Tommy Thompson, 4.18 Gameplay Evaluation Measures, Report of Dagstuhl Seminar Artificial and Computational Intelligence in Games: AI-Driven Game Design, p122 2018. [pdf]

Dan Ashlock, Cameron Browne, Simon Colton, Amy K Hoover, Jialin Liu, Simon M Lucas, Mark J Nelson, Diego Perez Liebana, Sebastian Risi, Jacob Schrum, Adam M Smith, Julian Togelius, Vanessa Volz, 4.1 Game Search Space Design and Representation, Report of Dagstuhl Seminar Artificial and Computational Intelligence in Games: AI-Driven Game Design, p38 2018. [pdf]

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, IEEE Transactions on Games (2017). [pdf] (SCI)

Raluca D. Gaina, Adrien Couëtoux, Dennis JNJ Soemers, Mark HM Winands, Tom Vodopivec, Florian Kirchgeßner, Jialin Liu, Simon M. Lucas, and Diego Perez-Liebana, The 2016 Two-Player GVGAI Competition, IEEE Transactions on Games (2017). [pdf] (SCI)

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] (EI)

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

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] (EI)

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] (EI)

Simon M. Lucas, Jialin Liu and Diego Perez-Liebana, Efficient noisy optimisation with the multi-sample and sliding window compact genetic algorithms, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-8. [pdf] (EI)

2016

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] (SCI)

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] (SCI)

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

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] (EI)

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). (EI)

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

2015

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] (EI)

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

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] (EI)

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] (EI)

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] (EI)

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] (EI)

2014 (EI)

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] (SCI)

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] (EI)

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] (EI)

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] (EI)

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] (EI)

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

2013

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] (EI)

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