Mesoscopic Modeling of Emergent Behavior - A Self-organizing Deliberative Minority Game

Recent research discussed several approaches to understand the relation between microscopic agent behavior and macroscopic multi–agent system (MAS) behavior. A structured methodology to derive these models will have impact on MAS design, evaluation and debugging. Current results have established the description of macroscopic behavior, including cooperation, by Rate Equations derived from markovian agent–states transitions. Emergent phenomena elude these descriptions. In this paper, we argue that mesoscopic modeling is needed to provide appropriate descriptions of emergent system behavior. The mesoscopic agent states reflect the emergent behavior and allow for a deliberative implementation of the rules and conditions which cause the MAS to self–organize as wanted. In a case study, we construct such a mesoscopic model for the socio-economic inspired Minority Game. The mesoscopic description leads us to a deliberative implementation, which exhibits equivalent self–organizing behavior, confirming our results.

[1]  Ali Cinar,et al.  Agent-Based Control of Spatially Distributed Chemical Reactor Networks , 2005, Engineering Self-Organising Systems.

[2]  Christopher A. Rouff,et al.  Formal Approaches to Agent-Based Systems , 2001, Lecture Notes in Computer Science.

[3]  M. Marsili,et al.  Minority Games: Interacting agents in financial markets , 2014 .

[4]  Yi Li,et al.  The minority game with variable payoffs , 2000, nlin/0002004.

[5]  Matteo Marsili,et al.  Minority Games: Interacting agents in financial markets (Oxford Finance Series) , 2005 .

[6]  Alcherio Martinoli,et al.  Macroscopic Modeling of Aggregation Experiments using Embodied Agents in Teams of Constant and Time-Varying Sizes , 2004, Auton. Robots.

[7]  H. Van Dyke Parunak,et al.  Information-Driven Phase Changes in Multi-agent Coordination , 2005, Engineering Self-Organising Systems.

[8]  N. Johnson,et al.  Self-Organized Segregation within an Evolving Population , 1998, cond-mat/9810142.

[9]  Franco Zambonelli,et al.  Software Engineering for Large-Scale Multi-Agent Systems , 2003, Lecture Notes in Computer Science.

[10]  Kristina Lerman,et al.  Mathematical Model of Foraging in a Group of Robots: Effect of Interference , 2002, Auton. Robots.

[11]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[12]  Franco Zambonelli,et al.  What Can Cellular Automata Tell Us about the Behavior of Large Multi-agent Systems? , 2002, SELMAS.

[13]  Rafael H. Bordini,et al.  Wayward agents in a commuting scenario (personalities in the minority game) , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[14]  Anand S. Rao,et al.  AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language , 1996, MAAMAW.

[15]  N. Kampen,et al.  Stochastic processes in physics and chemistry , 1981 .

[16]  Daniel Moldt,et al.  Goal Representation for BDI Agent Systems , 2004, PROMAS.

[17]  Michael Wooldridge,et al.  Model checking rational agents , 2004, IEEE Intelligent Systems.

[18]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[19]  Axel van Lamsweerde,et al.  Goal-Oriented Requirements Engineering: A Guided Tour , 2001, RE.

[20]  H. Van Dyke Parunak,et al.  Universality in multi-agent systems , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[21]  Li-Xin Zhong,et al.  Effects of contrarians in the minority game. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  T. Wolf,et al.  Emergence and self-organisation: a statement of similarities and differences , 2004 .

[23]  Jan Sudeikat,et al.  Modeling Minority Games with BDI Agents - A Case Study , 2005, MATES.

[24]  Gerhard Weiß,et al.  Agent orientation in software engineering , 2001, The Knowledge Engineering Review.

[25]  Kristina Lerman,et al.  Agent memory and adaptation in multi-agent systems , 2003, AAMAS '03.

[26]  W. Arthur.  Inductive Reasoning and Bounded Rationality , 1994 .

[27]  NICHOLAS R. JENNINGS,et al.  An agent-based approach for building complex software systems , 2001, CACM.

[28]  Amund Tveit,et al.  A survey of Agent-Oriented Software Engineering , 2001 .

[29]  Alcherio Martinoli,et al.  Modeling Swarm Robotic Systems , 2002, ISER.

[30]  Hiroshi Sasaki,et al.  Pheromone model: application to traffic congestion prediction , 2005, AAMAS '05.

[31]  G. Reents,et al.  A stochastic strategy for the minority game , 2001 .

[32]  Michael Rovatsos,et al.  Capturing agent autonomy in roles and XML , 2003, AAMAS '03.

[33]  Tom De Wolf,et al.  Engineering self-organising emergent systems with simulation-based scientific analysis , 2005 .

[34]  Kristina Lerman,et al.  Analysis of a Stochastic Model of Adaptive Task Allocation in Robots , 2004, Engineering Self-Organising Systems.

[35]  Nicholas R. Jennings,et al.  Agent Theories, Architectures, and Languages: A Survey , 1995, ECAI Workshop on Agent Theories, Architectures, and Languages.

[36]  Yi-Cheng Zhang,et al.  Emergence of cooperation and organization in an evolutionary game , 1997 .

[37]  David Stuart Robertson,et al.  Enacting the Distributed Business Workflows Using BPEL4WS on the Multi-agent Platform , 2005, MATES.

[38]  Richard Metzler,et al.  Evolutionary minority games: the benefits of imitation , 2002, cond-mat/0212481.

[39]  Walter Van de Velde,et al.  Agents Breaking Away , 1996, Lecture Notes in Computer Science.

[40]  Agostino Poggi,et al.  Jade - a fipa-compliant agent framework , 1999 .

[41]  Tao Zhou,et al.  Global optimization of minority game by intelligent agents , 2005 .

[42]  Kristina Lerman,et al.  Design and Mathematical Analysis of Agent-Based Systems , 2000, FAABS.

[43]  Michael Winikoff,et al.  Debugging multi-agent systems using design artifacts: the case of interaction protocols , 2002, AAMAS '02.

[44]  Carlos Angel Iglesias,et al.  A Survey of Agent-Oriented Methodologies , 1998, ATAL.

[45]  Hiroshi Sasaki,et al.  Pheromone Model: Application to Traffic Congestion Prediction , 2005, Engineering Self-Organising Systems.