The Effect of Mobility and Emotion on Interactions in Multi-Agent Systems

Simulating emotions within a group of agents has been shown to support co-operation in the prisoner’s dilemma game. Most work on simulating these emotions has focused on environments where the agents do not move, that is, they are static and their neighbours are fixed. However, it has also been shown in other work that when an agent is given the ability to move, then the type of the environment affects how co-operation evolves in the group of agents. In this paper, we investigate the combination of these two ideas in an experimental study that explores the effects on co-operation when autonomous agents that can show emotions are given the ability to move within structured environments. We observe that once mobility is introduced, different strategies become successful. Successful strategies respond quickly to defection, while not immediately reciprocating co-operation, regardless of the environment type. The further an agent travels, the higher its average payoff in a small world environment. The slower an agent is to copy another agent by imitating its strategy, the higher its increase in average payoff.

[1]  W. Hamilton,et al.  The Evolution of Cooperation , 1984 .

[2]  David G. Rand,et al.  Direct reciprocity in structured populations , 2012, Proceedings of the National Academy of Sciences.

[3]  Andrew Ortony,et al.  The Cognitive Structure of Emotions , 1988 .

[4]  Gerhard Weiss,et al.  Evolution of cooperation in arbitrary complex networks , 2014, AAMAS.

[5]  Thomas Rist,et al.  Integrating Models of Personality and Emotions into Lifelike Characters , 1999, IWAI.

[6]  F. C. Santos,et al.  Social diversity promotes the emergence of cooperation in public goods games , 2008, Nature.

[7]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[8]  N. Schwarz Emotion, cognition, and decision making , 2000 .

[9]  Gerhard Weiss,et al.  Valuation of Cooperation and Defection in Small-World Networks : A Behavioral Robotic Approach , 2014 .

[10]  Etienne B. Roesch,et al.  A Blueprint for Affective Computing: A Sourcebook and Manual , 2010 .

[11]  P. Petta,et al.  Computational models of emotion , 2010 .

[12]  David Watson,et al.  Emotion, mood, and temperament: Similarities, differences--and a synthesis. , 2001 .

[13]  Trevor J. M. Bench-Capon,et al.  Emotion as an Enabler of Co-operation , 2012, ICAART.

[14]  Mehdi Dastani,et al.  A Logic of Emotions for Intelligent Agents , 2007, AAAI.

[15]  Arne Traulsen,et al.  Partners or rivals? Strategies for the iterated prisoner's dilemma☆ , 2015, Games Econ. Behav..

[16]  D. Keltner,et al.  Functional Accounts of Emotions , 1999 .

[17]  Katia P. Sycara,et al.  The evolution of cooperation in self-interested agent societies: a critical study , 2011, AAMAS.

[18]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[19]  Gerhard Weiss,et al.  Influencing Social Networks: An Optimal Control Study , 2014, ECAI.