Organizing Scalable Adaptation in Serious Games

Serious games and other training applications have the requirement that they should be suitable for trainees with different skill levels. Current approaches either use human experts or a completely centralized approach for this adaptation. These centralized approaches become very impractical and will not scale if the complexity of the game increases. Agents can be used in serious game implementations as a means to reduce complexity and increase believability but without some centralized coordination it becomes practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper we show that using agent organizations to coordinate the agents is scalable and allows adaptation in very complex scenarios while making sure the storyline is preserved the right difficulty level for the trainee is preserved.

[1]  Tuomas Sandholm,et al.  Algorithm for optimal winner determination in combinatorial auctions , 2002, Artif. Intell..

[2]  Frank Dignum.  Agents for games and simulations , 2011, Autonomous Agents and Multi-Agent Systems.

[3]  Stacy Marsella,et al.  THESPIAN: An Architecture for Interactive Pedagogical Drama , 2005, AIED.

[4]  Steve Rabin,et al.  AI Game Programming Wisdom , 2002 .

[5]  Dave Moffat,et al.  Personality Parameters and Programs , 1997, Creating Personalities for Synthetic Actors.

[6]  Jenova Chen,et al.  Flow in games (and everything else) , 2007, CACM.

[7]  Michael Lees,et al.  Agents, games and HLA , 2006, Simul. Model. Pract. Theory.

[8]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[9]  Mehdi Dastani,et al.  2APL: a practical agent programming language , 2008, Autonomous Agents and Multi-Agent Systems.

[10]  John P. Lewis,et al.  The DEFACTO System: Training Tool for Incident Commanders , 2005, AAAI.

[11]  Gnana Bharathy,et al.  Human Behavior Models for Agents in Simulators and Games: Part II: Gamebot Engineering with PMFserv , 2006, Presence: Teleoperators & Virtual Environments.

[12]  Vincent Corruble,et al.  Extending Reinforcement Learning to Provide Dynamic Game Balancing , 2005 .

[13]  David L. Westbrook,et al.  Intelligent modeling of the user in interactive entertainment , 2002 .

[14]  Robin Hunicke,et al.  The case for dynamic difficulty adjustment in games , 2005, ACE '05.

[15]  Robert Trappl,et al.  Creating Personalities for Synthetic Actors , 1997, Lecture Notes in Computer Science.

[16]  Olivier Boissier,et al.  S-MOISE+: A Middleware for Developing Organised Multi-agent Systems , 2005, AAMAS Workshops.

[17]  Frank Dignum,et al.  Adaptive Serious Games Using Agent Organizations , 2009, AGS.

[18]  Eric O. Postma,et al.  Adaptive game AI with dynamic scripting , 2006, Machine Learning.

[19]  M. V. Dignum,et al.  A Model for Organizational Interaction: based on Agents, founded in Logic , 2000 .

[20]  Marc Cavazza,et al.  Characters in Search of an Author: AI-Based Virtual Storytelling , 2001, International Conference on Virtual Storytelling.

[21]  Torbjörn Lager,et al.  DEAL: dialogue management in SCXML for believable game characters , 2007, Future Play.

[22]  Andrew Stern,et al.  Failing Believably: Toward Drama Management with Autonomous Actors in Interactive Narratives , 2006, TIDSE.

[23]  Jaime Simão Sichman,et al.  Coordination, Organizations, Institutions, and Norms in Multi-Agent Systems: AAMAS 2005 International Workshops on Agents, Norms, and Institutions for ... Papers (Lecture Notes in Computer Science) , 2006 .

[24]  Brian Magerko,et al.  AI Characters and Directors for Interactive Computer Games , 2004, AAAI.

[25]  Stacy Marsella,et al.  Thespian: using multi-agent fitting to craft interactive drama , 2005, AAMAS '05.