AI in Human-computer Gaming: Techniques, Challenges and Opportunities
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Abstract (arXiv)
With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five and AlphaStar, beating professional human players. The rapid development of human-computer gaming AIs indicate a big step of decision making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question raises: what are the possible challenges of current techniques in human-computer gaming, and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs and real time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope this brief review can provide an introduction for beginners, and inspire insights for researchers in the field of AI in human-computer gaming.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 37 of its 74 references that are in the map. First posted on arXiv 15 Nov 2021.
Builds on
37 of 74 references.
References · 37
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
- 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
- 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
- 2020Pre-trained Models for Natural Language Processing: A Surveycited by 505
- 2017A Brief Survey of Deep Reinforcement Learningcited by 483
- 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
- 2020Agent57: Outperforming the Atari Human Benchmarkcited by 303
- 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 279
- 2018Human-level performance in first-person multiplayer games with population-based deep reinforcement learningcited by 264
25 more
- 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 233
- 2019Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systemscited by 205
- 2019Mastering Complex Control in MOBA Games with Deep Reinforcement Learningcited by 161
- 2020Continual Lifelong Learning in Natural Language Processing: A Surveycited by 152
- 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
- 2020Combining Deep Reinforcement Learning and Search for Imperfect-Information Gamescited by 114
- 2021M6: A Chinese Multimodal Pretrainercited by 91
- 2015Massively Parallel Methods for Deep Reinforcement Learningcited by 82
- 2020Towards Playing Full MOBA Games with Deep Reinforcement Learningcited by 78
- 2020Real World Games Look Like Spinning Topscited by 66
- 2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 66
- 2020Suphx: Mastering Mahjong with Deep Reinforcement Learningcited by 58
- 2019α-Rank: Multi-Agent Evaluation by Evolutioncited by 51
- 2018Re-evaluating Evaluationcited by 47
- 2017Safe and Nested Subgame Solving for Imperfect-Information Gamescited by 41
- 2021M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretrainingcited by 31
- 2013Solving Imperfect Information Games Using Decompositioncited by 29
- 2020SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft IIcited by 22
- 2018Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learningcited by 11
- 2019Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Gamescited by 11
- 2021Measuring the Non-Transitivity in Chesscited by 10
- 2020Distributed Deep Reinforcement Learning: An Overviewcited by 9
- 2020Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learningcited by 9
- 2017Symmetric Decomposition of Asymmetric Gamescited by 4
- 2018Bayesian Transfer Reinforcement Learning with Prior Knowledge Rulescited by 2
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