Publications:

12. Hui Wang. "Searching by Learning: Exploring Artificial General Intelligence on Small Board Games by Deep Reinforcement Learning". PhD thesis, to appear. 2021, Leiden.

11. Hui Wang, Mike Preuss, and Aske Plaat. "Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning". Pacific Rim International Conferences on Artificial Intelligence 2021, Hanoi, Vietnam, November 2021. Accepted.

10. Hui Wang, Mike Preuss, and Aske Plaat. ”Warm-start alphazero self-play search enhancements.” In International Conference on Parallel Problem Solving from Nature, pp. 528-542. Springer, Cham, 2020.

9. Hui Wang, Mike Preuss, Michael Emmerich, and Aske Plaat. ”Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning.” 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, 2020, pp.149-152, doi: 10.1109/SYNASC51798.2020.00033.

8. Hui Wang, Michael Emmerich, Mike Preuss, and Aske Plaat. ”Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?.” arXiv preprint arXiv:2003.05988 (2020). in submission.

7. Hui Wang, Michael Emmerich, Mike Preuss, and Aske Plaat. ”Hyper-parameter sweep on alphazero general.” arXiv preprint arXiv:1903.08129 (2019).

6. Hui Wang, Michael Emmerich, Mike Preuss, and Aske Plaat. ”Alternative loss functions in alphazero-like self-play.” In 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp.155-162. IEEE, 2019.

5. Hui Wang, Michael Emmerich, and Aske Plaat. ”Assessing the potential of classical Q-learning in general game playing.” In Benelux Conference on Artificial Intelligence, pp. 138-150. Springer, Cham,2018. Best Paper Award

4. Hui Wang, Michael Emmerich, and Aske Plaat. ”Monte carlo q-learning for general game playing.”arXiv preprint arXiv:1802.05944 (2018).

3. Hui Wang, Yanni Tang, Jiamou Liu, and Wu Chen. ”A search optimization method for rule learning in board games.” In Pacific Rim International Conference on Artificial Intelligence, pp. 174-181. Springer, Cham, 2018.

2. Hui Wang, Liang Li, Long-yun Gao, and Wu Chen. ”A Complex Networked Method of Sorting Negotiation Demand Based on Answer Set Programs.” Intelligent Automation&Soft Computing (2017): 1-6.

1. Fang Yang, Hui Wang, Yanni Tang, Jiamou Liu, and Wu Chen. ”A consensus value approach for influence maximization in social networks.” In 2017 IEEE International Conference on Agents (ICA), pp. 8-13. IEEE, 2017