Game AI Competitions: Motivation for the Imitation Game-Playing Competition

Games have played crucial role in advancing research in Artificial Intelligence and tracking its progress. In this article, a new proposal for game AI competition is presented. The goal is to create computer players which can learn and mimic the behavior of particular human players given access to their game records. We motivate usefulness of such an approach in various aspects, e.g., new ways of understanding what constitutes the human-like AI or how well it fits into the existing game production workflows. This competition may integrate many problems such as learning, representation, approximation and compression of AI, pattern recognition, knowledge extraction etc. This leads to multi-directional implications both on research and industry. In addition to the proposal, we include a short survey of the available game AI competitions.

[1]  Julia Fink,et al.  Anthropomorphism and Human Likeness in the Design of Robots and Human-Robot Interaction , 2012, ICSR.

[2]  Jakub Kowalski,et al.  Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes , 2020, ArXiv.

[3]  Ben Goertzel,et al.  Artificial General Intelligence: Concept, State of the Art, and Future Prospects , 2009, J. Artif. Gen. Intell..

[4]  Demis Hassabis,et al.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.

[5]  Maciej Swiechowski,et al.  Specialized vs. Multi-game Approaches to AI in Games , 2014, IEEE Conf. on Intelligent Systems.

[6]  R. Penrose,et al.  How Long Until Human-Level AI ? Results from an Expert Assessment , 2011 .

[7]  Monty Newborn 2007: Deep Junior Deep Sixes Deep Fritz in Elista, 4–2 , 2011 .

[8]  Radoslaw Martin Cichy,et al.  Resolving human object recognition in space and time , 2014, Nature Neuroscience.

[9]  Hongyu Kuang,et al.  Macro action selection with deep reinforcement learning in StarCraft , 2019, AIIDE.

[10]  Andrzej Janusz,et al.  Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms , 2018, 2018 IEEE Conference on Computational Intelligence and Games (CIG).

[11]  Jichen Zhu,et al.  Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation , 2018, 2018 IEEE Conference on Computational Intelligence and Games (CIG).

[12]  H. Francis Song,et al.  The Hanabi Challenge: A New Frontier for AI Research , 2019, Artif. Intell..

[13]  Tomohiro Harada,et al.  Self-Play for Training General Fighting Game AI , 2019, 2019 Nicograph International (NicoInt).

[14]  Chuang Gan,et al.  Visual Concept-Metaconcept Learning , 2020, NeurIPS.

[15]  Jonathan Schaeffer,et al.  Checkers Is Solved , 2007, Science.

[16]  Dominik Slezak,et al.  Toward an Intelligent HS Deck Advisor: Lessons Learned from AAIA’ 18 Data Mining Competition , 2018, 2018 Federated Conference on Computer Science and Information Systems (FedCSIS).

[17]  Santiago Ontañón,et al.  The First microRTS Artificial Intelligence Competition , 2018, AI Mag..

[18]  Dario Amodei,et al.  An Empirical Model of Large-Batch Training , 2018, ArXiv.

[19]  Bruce R. Schatz,et al.  Concept Extraction in the Interspace Prototype , 1999 .

[20]  Edmond P. Odescalchi Can a Machine Think , 1958 .

[21]  Marcel Roeloffzen,et al.  Hanabi is NP-hard, even for cheaters who look at their cards , 2016, Theor. Comput. Sci..

[22]  Maciej Swiechowski,et al.  Self-Adaptation of Playing Strategies in General Game Playing , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[23]  Wojciech Jaskowski,et al.  ViZDoom: A Doom-based AI research platform for visual reinforcement learning , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[24]  Monty Newborn,et al.  Kasparov versus Deep Blue - computer chess comes of age , 1996 .

[25]  Kyung-Joong Kim,et al.  Recent Advances in General Game Playing , 2015, TheScientificWorldJournal.

[26]  Dominik Slezak,et al.  Grail: A Framework for Adaptive and Believable AI in Video Games , 2018, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[27]  P. Hingston Believable Bots: Can Computers Play Like People? , 2012 .

[28]  Julian Togelius,et al.  Blood Bowl: A New Board Game Challenge and Competition for AI , 2019, 2019 IEEE Conference on Games (CoG).

[29]  Fumihiko Hashimoto,et al.  Can Machine Think , 1995 .

[30]  Peng Zhang,et al.  The Angry Birds AI Competition , 2015, AI Mag..

[31]  H. Jaap van den Herik,et al.  Games solved: Now and in the future , 2002, Artif. Intell..

[32]  Dominik Slezak,et al.  Granular Games in Real-Time Environment , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

[33]  Dominik Slezak,et al.  Utilizing Hybrid Information Sources to Learn Representations of Cards in Collectible Card Video Games , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

[34]  Michael R. Genesereth,et al.  General Game Playing: Overview of the AAAI Competition , 2005, AI Mag..

[35]  Santiago Ontañón,et al.  A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[36]  Omar Syed,et al.  ARIMAA - A NEW GAME DESIGNED TO BE DIFFICULT FOR COMPUTERS , 2003 .

[37]  Suresh Bandi,et al.  Solving the Complexity of Geometry Friends by Using Artificial Intelligence , 2020 .

[38]  Luciano Reis Coutinho,et al.  Hierarchical Reinforcement Learning With Monte Carlo Tree Search in Computer Fighting Game , 2019, IEEE Transactions on Games.

[39]  Julian Togelius,et al.  Generative design in minecraft (GDMC): settlement generation competition , 2018, FDG.

[40]  Erik T. Mueller,et al.  Watson: Beyond Jeopardy! , 2013, Artif. Intell..

[41]  Maciej Swiechowski,et al.  Introducing LogDL – Log Description Language for Insights from Complex Data , 2020, 2020 15th Conference on Computer Science and Information Systems (FedCSIS).

[42]  Simon M. Lucas,et al.  A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[43]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[44]  Jacob Whitehill Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle , 2018, AAAI Workshops.

[45]  Nannan Li,et al.  Learning Battles in ViZDoom via Deep Reinforcement Learning , 2018, 2018 IEEE Conference on Computational Intelligence and Games (CIG).

[46]  Marek Wydmuch,et al.  ViZDoom Competitions: Playing Doom From Pixels , 2018, IEEE Transactions on Games.

[47]  Dominik Slezak,et al.  Knowledge Pit - A Data Challenge Platform , 2015, CS&P.

[48]  Wei Xu,et al.  CNN-RNN: A Unified Framework for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Wojciech M. Czarnecki,et al.  Grandmaster level in StarCraft II using multi-agent reinforcement learning , 2019, Nature.

[50]  Liuqing Yang,et al.  Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond , 2016, IEEE/CAA Journal of Automatica Sinica.

[51]  Allen Newell,et al.  Chess-Playing Programs and the Problem of Complexity , 1958, IBM J. Res. Dev..

[52]  Julian Togelius,et al.  Ieee Transactions on Computational Intelligence and Ai in Games the 2014 General Video Game Playing Competition , 2022 .

[53]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[54]  Ayesha Irfan,et al.  Evolving Levels for General Games Using Deep Convolutional Generative Adversarial Networks , 2019, 2019 11th Computer Science and Electronic Engineering (CEEC).