Publications

  • Journal Papers
  • Invention Patent

1. Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N. Yannakakis, Ju- lian Togelius, “Deep Learning for Procedural Content Generation,” Neural Computing and Applications (NCAA), 2020. (Early Access)

2. Jialin Liu, Ke Tang, Xin Yao, “Robust Optimisation in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives,” IEEE Computational Intelligence Magazine (IEEE CIM), 2020. (Accepted)

3. 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,” in IEEE Transactions on Games, vol. 11, no. 3, pp. 195-214, Sept. 2019.

4. Chiara Sironi, Jialin Liu and Mark Winands, “Self-Adaptive Monte-Carlo Tree Search in General Game Playing,” in IEEE Transactions on Games, vol. 12, no. 2, pp. 132-144, June 2020.

5. 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,” in IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 2618-2633, Oct. 2018.

6. Philipp Rohlfshagen, Jialin Liu, Diego Perez-Liebana and Simon M. Lucas, “Pac-Man Conquers Academia: Two Decades of Research Using a Classic Arcade Game,” in IEEE Transactions on Games, vol. 10, no. 3, pp. 233-256, Sept. 2018.

7. Raluca D. Gaina, Adrien Coutoux, Dennis JNJ Soemers, Mark HM Winands, Tom Vodopivec, Florian Kirchgener, Jialin Liu, Simon M. Lucas, and Diego Perez-Liebana, “The 2016 Two- Player GVGAI Competition,” in IEEE Transactions on Games, vol. 10, no. 2, pp. 209-220, June 2018.

8. Marie-Liesse Cauwet, Jialin Liu*, Baptiste Rozi`ere, Olivier Teytaud, “Algorithm Portfolios for Noisy Optimization,” Annals of Mathematics and Artificial Intelligence (AMAI), vol. 76, no 1-2, p. 143-172.

9. 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.

10. 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, 2014.

 

Conference papers


1. Jialin Liu, Antoine Moreau, Mike Preuss, Jeremy Rapin, Baptiste Roziere, Fabien Teytaud, Olivier Teytaud, “Versatile Black-Box Optimization,” Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), Association for Computing Machinery, New York, NY, USA, 620–628.

2. Jacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu, Simon Lucas, Sebastian Risi, “Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks,” Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), Association for Computing Machinery, New York, NY, USA, 148–156.

3. Tianye Shu, Ziqi Wang, Jialin Liu*, Xin Yao, “A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros,” 2020 IEEE Congress on Evolutionary Computation (CEC 2020). IEEE. (Tianye Shu and Ziqi Wang were 3rd year UG students)

4. Han Zhang, Jialin Liu, Xin Yao, “A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem,” Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), LNCS 12270, pp. 454–467, 2020. (Han Zhang was 2nd year Master student)

5. Qingquan Zhang, Feng Wu, Yang Tao, Jiyuan Pei, Jialin Liu*, Xin Yao, “D-MAENS2: A Self- adaptive D-MAENS Algorithm with Better Decision Diversity”, The 2020 IEEE Symposium Series on Computational Intelligence. (Accepted) (Qingquan Zhang was 2nd year Master student)

6. Chenhao Li, Jiyuan Pei, Qingquan Zhang, Jialin Liu, Xin Yao, “An Extendable Platform for Routing Problem: Optimisation, Evaluation and Solution Visualisation”, The 2020 IEEE Symposium Series on Computational Intelligence. (Accepted) (Chenhao Li and Jiyuan Pei were 4th year UG students)

7. Jialin Liu and Xin Yao, “Self-adaptive Decomposition and Incremental Hyperparameter Tun- ing Across Multiple Problems,” Proceedings of the IEEE Symposium Series on Computational Intelligence, Xiamen, China, 2019, pp. 1590–1597. IEEE.

8. Jialin Liu and Olivier Teytaud, “A Simple Yet Effective Resampling Rule in Noisy Evolution- ary Optimization,” Proceedings of the IEEE Symposium Series on Computational Intelligence, Xiamen, China, 2019, pp. 689-696. IEEE.

9. Jialin Liu and Olivier Teytaud, “Efficient Decision Making under Uncertainty in a Power System Investment Problem,” Proceedings of the IEEE Symposium Series on Computational Intelligence, Xiamen, China, 2019, pp. 697–704. IEEE.

10. Hao Tong, Jialin Liu and Xin Yao, “Algorithm Portfolio for Individual-based Surrogate- assisted Evolutionary Algorithms,” Proceedings of 2019 Genetic and Evolutionary Computation Conference (GECCO 2019), Kyoto, pp. 943–950, ACM Press. (Hao Tong was 2nd year Master student)

11. Hao Tong, Changwu Huang, Jialin Liu and Xin Yao, “Voronoi-based Efficient Surrogate- assisted Evolutionary Algorithm for Very Expensive Problems,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2019), Wellington, New Zealand, 2019, pp. 1996–2003. (Hao Tong was 2nd year Master student)

12. Ivan Bravi, Simon M. Lucas, Diego Perez-Liebana and Jialin Liu, “Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor,” Proceedings of the IEEE Conference on Games (CoG 2019), London, United Kingdom, 2019, pp. 1–8.

13. 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.

14. 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 2018 Annual Conference on Genetic and Evolutionary Computation (GECCO 2018), Kyoto, pp. 221228, ACM Press. (Best Paper Award)

15. Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu and Diego Perez Liebana, “Deep reinforcement learning in the General Video Game AI framework,” Proceedings of the IEEE Computational Intelligence and Games Conference (CIG 2018), Maastricht, 2018, pp. 1–8.

16. Ivan Bravi, Diego Perez, Simon Lucas and Jialin Liu, “Shallow decision-making analysis in General Video Game Playing,” Proceedings of the IEEE Computational Intelligence and Games Conference (CIG 2018), Maastricht, 2018, pp. 1–8.

17. 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 (EvoApplications 2018), Lecture Notes in Computer Science, vol 10784. Springer, Cham.

18. Simon M. Lucas, Jialin Liu, Diego Perez-Liebana, “The N-tuple Bandit Evolutionary Algo- rithm for Game Agent Optimisation,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC’18), Rio de Janeiro, 2018, pp. 1–9.

19. Marie-Liesse Cauwet, Jeremie Decock, Jialin Liu, Olivier Teytaud, “Direct Model Predictive Control: A Theoretical and Numerical Analysis,” Proceedings of the 20th Power Systems Computation Conference (PSCC 2018), Dublin, 2018, pp. 1–7.

20. Jialin Liu, Julian Togelius, Diego Perez-Liebana and Simon M. Lucas, “Evolving Game Skill-Depth using General Video Game AI Agents,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC’17), San Sebastian, 2017, pp. 2299–2307.

21. Jialin Liu, Diego Perez-Liebana and Simon M. Lucas, “Bandit-Based Random Mutation Hill-Climbing,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC’17), San Sebastian, 2017, pp. 2145–2151.

22. Kamolwan Kunanusont, Raluca D. Gaina, Jialin Liu, Diego Perez-Liebana and Simon M. Lucas, “The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC’17), San Sebastian, 2017, pp. 2201–2208. (shortlisted for Best Paper Award from 347 accepted papers)

23. 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 Applications of Evolutionary Computation (EvoApplications 2017), Lecture Notes in Computer Science, vol 10199. Springer, Cham.

24. 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), Honolulu, 2017, pp. 1–8.

25. Jialin Liu, Oliver Teytaud, Tristan Cazenave, “Fast Seed-Learning Algorithms for Games,” Proceedings of the 9th International Conference on Computers and Games (CG 2016), Lecture Notes in Computer Science, vol 10068. Springer, Cham.

26. Tristan Cazenave, Jialin Liu, Fabien Teytaud, Olivier Teytaud, “Learning Opening Books in Partially Observable Games: Using Random Seeds in Phantom Go,” Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2016), Santorini, 2016, pp. 1–7.

27. Jialin Liu, Diego P ́erez-Li ́ebana and Simon M. Lucas, “Rolling Horizon Coevolutionary Planning for Two-Player Video Games,” Proceedings of the 8th Computer Science and Electronic Engineering (CEEC 2016), Colchester, 2016, pp. 174–179.

28. J ́er ́emie 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 2015), Madrid, pp. 6162, ACM Press.

29. Tristan Cazenave, Jialin Liu, Olivier Teytaud, “The Rectangular Seeds of Domineering,” 2015 IEEE Computational Intelligence and Games Conference (CIG 2015), Tainan, 2015, pp. 530–531.

30. 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,” Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Istanbul, 2015, pp. 1–7.

31. 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 1.01n Resamplings at Iteration n and Reach the −1 Slope,” Proceedings of IEEE Congress on Evolutionary Computation (CEC’15), Sendai, 2015, pp. 338–345.

32. David L. St-Pierre, Jialin Liu and Olivier Teytaud, “Nash Reweighting of Monte Carlo Sim- ulations: Tsumego,” Proceedings of IEEE Congress on Evolutionary Computation (CEC’15), Sendai, 2015, pp. 1458–1465.

33. Jean-Joseph Christophe, J ́er ́emie Decock, Jialin Liu and Olivier Teytaud, “Variance Reduction in Population-Based Optimization: Application to Unit Commitment,” Proceedings of Biennial International Conference on Artificial Evolution (EA 2015), Lecture Notes in Computer Science, vol 9554. Springer, Cham.

34. Jialin Liu, David L. St-Pierre and Olivier Teytaud, “A Mathematically Derived Number of Resamplings for Noisy Optimization,” Proceedings of the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO 2014), Vancouver, pp. 6162, ACM Press.

35. Jialin Liu and Olivier Teytaud, “Meta Online Learning: Experiments on a Unit Commitment Problem,” Proceedings of European Symposium on Artificial Neural Networks (ESANN 2014), Computational Intelligence and Machine Learning, Bruges (Belgium), 23–25 April 2014.

36. David Auger, Jialin Liu, Sylvie Ruette, David L. St-Pierre and Olivier Teytaud, “Sparse Binary Zero-sum Games,” Proceedings of the Sixth Asian Conference on Machine Learning (ACML 2014), PMLR 39:173–188, 2015.

37. Marie-Liesse Cauwet, Jialin Liu and Olivier Teytaud, “Algorithm Portfolios for Noisy Opti- mization: Compare Solvers Early,” Proceedings of the International Conference on Learning and Intelligent Optimization (LION 2014), Lecture Notes in Computer Science, vol 8426. Springer, Cham.

38. David L. St-Pierre and Jialin Liu, “Differential Evolution Algorithm Applied to Non- stationary Bandit Problem,” Proceedings of IEEE Congress on Evolutionary Computation (CEC’14), Beijing, China, pp. 2397–2403, IEEE.

39. Sandra Astete-Morales, Jialin Liu and Olivier Teytaud, “Noisy Optimization Convergence Rates,” Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013), Amsterdam, The Netherlands, pp. 223224, ACM Press.

40. Sandra Astete-Morales, Jialin Liu and Olivier Teytaud, “Log-log Convergence for Noisy Optimization,” Proceedings of Biennial International Conference on Artificial Evolution (EA 2013). Lecture Notes in Computer Science, vol 8752. Springer, Cham.

 

Other publications


1. Jialin Liu, Tom Schaul, Pieter Spronck and Julian Togelius, “Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511)”, Dagstuhl Reports, Volume 9, Issue 12.

2. Vanessa Volz, Dan Ashlock, Simon Colton, Steve Dahlskog, Jialin Liu, Simon M. Lucas, Diego Perez Liebana and Tommy Thompson, “4.18 Gameplay Evaluation Measures,” Report of Dagstuhl Seminar Artificial and Computational Intelligence in Games: AI-Driven Game Design, Volume 7, Issue 11, pp. 105-107, 2018.

3. 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 and Vanessa Volz, “4.1 Game Search Space Design and Representation,” Report of Dagstuhl Seminar Artificial and Computational Intelligence in Games: AI-Driven Game Design, Volume 7, Issue 11, pp. 93-95, 2018.

裴季源李晨昊,刘佳琳,姚新。联想词的推荐方法、装置、计算机设备和存储介质,国家发明专利,实审公开,202010182211.7。

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