SISINE: A Negotiation Training Dedicated Multi-Player Role-Playing Platform Using Artificial Intelligence Skills

In recent decades, a number of trainers have used role-playing games to teach negotiation skills. The SISINE Project – funded by the EU Leonardo Program - has developed a teaching methodology making it possible to conduct this kind of approaches in a virtual environment. The teaching methodology exploits a specially-developed technology platform allowing a small community of players to communicate, to interact and to play online in order to acquire basic notions and rules about negotiation and how to apply this knowledge. A part of SISINE project has investigated Artificial Intelligence issued techniques in order to evaluate implementation’s possibility of computer-controlled “artificial players” embodying some intelligent behaviors. This chapter presents the first results of those investigations.

[1]  Mark Klein,et al.  Protocols for Negotiating Complex Contracts , 2003, IEEE Intell. Syst..

[2]  Catholijn M. Jonker,et al.  An Agent Architecture for Multi-Attribute Negotiation , 2001, IJCAI.

[3]  David W. Johnson,et al.  Cooperative Versus Competitive Efforts and Problem Solving , 1995 .

[4]  Nicholas R. Jennings,et al.  Acquiring user tradeoff strategies and preferences for negotiating agents: A default-then-adjust method , 2006, Int. J. Hum. Comput. Stud..

[5]  Carles Sierra,et al.  Agent-Mediated Electronic Commerce , 2004, Autonomous Agents and Multi-Agent Systems.

[6]  Thomas G. Dietterich.  Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..

[7]  Long Ji Lin,et al.  Self-improving reactive agents based on reinforcement learning, planning and teaching , 1992, Machine Learning.

[8]  Robert E. Kraut,et al.  Project massive: self-regulation and problematic use of online gaming , 2007, CHI.

[9]  Richard S. Sutton,et al.  Dimensions of Reinforcement Learning , 1998 .

[10]  Catholijn M. Jonker,et al.  Automated multi-attribute negotiation with efficient use of incomplete preference information , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[11]  Sara de Freitas,et al.  Online gaming as an educational tool in learning and training , 2007, Br. J. Educ. Technol..

[12]  William M. Fox,et al.  Effective Group Problem Solving: How to Broaden Participation, Improve Decision Making, and Increase Commitment to Action , 1987 .

[13]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[14]  Pattie Maes,et al.  Agent-Mediated Integrative Negotiation for Retail Electronic Commerce , 1998, AMET.

[15]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[16]  James A. Anderson,et al.  An Introduction To Neural Networks , 1998 .

[17]  Claude F. Touzet,et al.  Neural reinforcement learning for behaviour synthesis , 1997, Robotics Auton. Syst..

[18]  Tuomas Sandholm,et al.  Distributed rational decision making , 1999 .

[19]  Chris Watkins,et al.  Learning from delayed rewards , 1989 .

[20]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[21]  R. Lewicki.  Teaching Negotiation and Dispute Resolution in Colleges of Business: The State of the Practice , 1997 .

[22]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[23]  Steven Douglas Whitehead,et al.  Reinforcement learning for the adaptive control of perception and action , 1992 .

[24]  Angelo Rega,et al.  Le nuove macchine per apprendere: simulazioni al computer, robot e videogiochi multi-utente. Alcuni prototipi , 2007 .

[25]  李幼升,et al.  Ph , 1989 .

[26]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[27]  Dan W. Patterson,et al.  Artificial Neural Networks: Theory and Applications , 1998 .

[28]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .