Human-Machine Cooperation Loop in Game Playing

—This paper presents a new concept for human-machine iterative cooperation based on the Upper Confidence Bounds Applied to Trees method. Analysis of such human-computer cooperation may potentially lead to pertinent insights related to performance improvement of both the human subject and an artificial agent (machine) during certain kinds of strategic interactions. While the experiments described in this paper refer to the so-called General Game Playing (being a certain embodiment of multi-game playing) the overall idea of proposed human-machine cooperation loop extends beyond game domain and can, in principle, be implemented in the form of a flexible general-purpose system applicable to cooperative problem solving or strategic interactions of various kinds. The concept proposed in this study is evaluated by means of a direct involvement of human subjects in specifically defined cooperative environment, which provides vast opportunity to learn from and cooperate with an artificial game playing agent under the certain rules of cooperation. The choice of games is seemingly important to this kind of experiment. Although the participants played better with the assistance of the machine in some of the games, they lost the track in subsequent matches when the assistance was (intentionally) no longer available. Possible reasons for such an activity pattern are discussed in the conclusions. Three design iterations showing evolution of the experiment setup are presented in the paper. The analysis of cooperative and non- cooperative matches reveals different patterns for each chosen game characterized by various levels of advantage gained by means of cooperation.

[1]  Maciej Swiechowski,et al.  Fast interpreter for logical reasoning in general game playing , 2016, J. Log. Comput..

[2]  Yew-Soon Ong,et al.  Specialization of a UCT-Based General Game Playing Program to Single-Player Games , 2016, IEEE Transactions on Computational Intelligence and AI in Games.

[3]  Jacek Madziuk,et al.  Risk-Aware Project Scheduling for Projects with Varied Risk Levels , 2015, SSCI.

[4]  Jacek Mandziuk,et al.  A New Approach to Security Games , 2015, ICAISC.

[5]  Hussein A. Abbass,et al.  Human-Machine Cooperation in General Game Playing , 2015, ACHI 2015.

[6]  Slawomir Zadrozny,et al.  Proactive and reactive risk-aware project scheduling , 2014, 2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI).

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

[8]  Carmel Domshlak,et al.  Monte-Carlo Tree Search: To MC or to DP? , 2014, ECAI.

[9]  M. Argyle.  Cooperation (Psychology Revivals) : The basis of sociability , 2013 .

[10]  Hao Chen,et al.  A Novel Human–Machine Collaborative Interface for Aero-Engine Pipe Routing , 2013, IEEE Transactions on Industrial Informatics.

[11]  Zoran Popovic,et al.  Evaluating Competitive Game Balance with Restricted Play , 2012, AIIDE.

[12]  Mausam,et al.  LRTDP Versus UCT for Online Probabilistic Planning , 2012, AAAI.

[13]  Thomas Keller,et al.  PROST: Probabilistic Planning Based on UCT , 2012, ICAPS.

[14]  Michèle Sebag,et al.  The grand challenge of computer Go , 2012, Commun. ACM.

[15]  Allen Klinger,et al.  Human-Machine Interactive Systems , 2012, Languages and Information Systems.

[16]  Jacek Mandziuk,et al.  Towards Cognitively Plausible Game Playing Systems , 2011, IEEE Computational Intelligence Magazine.

[17]  Jacek Mandziuk,et al.  Knowledge-Free and Learning-Based Methods in Intelligent Game Playing , 2010, Studies in Computational Intelligence.

[18]  Elizabeth S. Spelke,et al.  Foundations of cooperation in young children , 2008, Cognition.

[19]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

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

[21]  C. Eden,et al.  Robotically assisted surgery , 2005, BJU international.

[22]  Frank L. Lewis,et al.  Aircraft Control and Simulation , 1992 .

[23]  Graham Kendall,et al.  Editorial: IEEE Transactions on Computational Intelligence and AI in Games , 2015, IEEE Trans. Comput. Intell. AI Games.

[24]  R. A. Bailey,et al.  Design of comparative experiments , 2008 .

[25]  Jacek Mandziuk,et al.  Computational Intelligence in Mind Games , 2007, Challenges for Computational Intelligence.

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

[27]  Michael Buro,et al.  The evolution of strong othello programs , 2002, IWEC.

[28]  Feng-Hsiung Hsu,et al.  Behind Deep Blue: Building the Computer that Defeated the World Chess Champion , 2002 .