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2020 – today
- 2022
[j1]Zuozhi Yang
, Santiago Ontañón:
An Experimental Survey on Methods for Integrating Scripts Into Adversarial Search for RTS Games. IEEE Trans. Games 14(2): 117-125 (2022)- 2021
- [c9]Zuozhi Yang, Santiago Ontañón:
Contextual Combinatorial Bandits in Real-Time Strategy Games. CoG 2021: 1-9 - 2020
- [c8]Zuozhi Yang, Santiago Ontañón:
Bandit-Based Policy Optimization for Monte Carlo Tree Search in RTS Games. AIIDE Workshops 2020 - [c7]Zuozhi Yang, Santiago Ontañón:
Are Strong Policies Also Good Playout Policies? Playout Policy Optimization for RTS Games. AIIDE 2020: 144-150
2010 – 2019
- 2019
- [c6]Zuozhi Yang, Santiago Ontañón:
Extracting Policies from Replays to Improve MCTS in Real Time Strategy Games. KEG@AAAI 2019: 2-8 - [c5]Zuozhi Yang, Santiago Ontañón:
Guiding Monte Carlo Tree Search by Scripts in Real-Time Strategy Games. AIIDE 2019: 100-107 - 2018
- [c4]Zuozhi Yang, Santiago Ontañón:
Learning Map-Independent Evaluation Functions for Real-Time Strategy Games. CIG 2018: 1-7 - 2017
- [c3]Zuozhi Yang, Todd W. Neller:
A Monte Carlo Localization Assignment Using a Neato Vacuum with ROS. AAAI 2017: 4803-4805 - 2016
- [c2]Todd W. Neller, Colin M. Messinger, Zuozhi Yang:
Learning and Using Hand Abstraction Values for Parameterized Poker Squares. AAAI 2016: 4095-4100 - [c1]Todd W. Neller, Zuozhi Yang, Colin M. Messinger, Calin Anton, Karo Castro-Wunsch, William Maga, Steven A. Bogaerts, Robert Arrington, Clay Langley:
Monte Carlo Approaches to Parameterized Poker Squares. Computers and Games 2016: 22-33
Coauthor Index
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