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Environmental effects on simulated emotional and moody agents

Part of: Adaptive Learning Agents 2016

Published online by Cambridge University Press:  24 August 2017

Joe Collenette ,
Katie Atkinson ,
Daan Bloembergen  and
Karl Tuyls
Joe Collenette
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: j.m.collenette@liverpool.ac.uk
Katie Atkinson
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: j.m.collenette@liverpool.ac.uk
Daan Bloembergen
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: j.m.collenette@liverpool.ac.uk
Karl Tuyls
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: j.m.collenette@liverpool.ac.uk
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Abstract

Psychological models have been used to simulate emotions within agents as part of the decision-making process. The body of this work has focussed on applying the process of decision making using emotions to social dilemmas, notably the Prisoner’s Dilemma. Previous work has focussed on agents which do not move around, with an initial analysis on how mobility and the environment can affect the decisions chosen. Additionally simulated mood has been introduced to the decision-making process. Exploring simulated emotions and mood to inform the decision-making process in multi-agent systems allows us to explore in further detail how outside influences can have an effect on different strategies. We expand and clarify aspects of how agents are affected by environmental differences. We show how emotional characters settle on an outcome without deviation by providing a formal proof. We validate how the addition of mood increases cooperation, while also showing how small groups achieve this quicker than large groups. Once pure defectors are added, to test the resilience of the cooperation achieved, we see that while agents with a low starting mood achieve a payoff closest to the pure defectors, they are reduced in numbers the most by the pure defectors.

Type
Adaptive and Learning Agents
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e19
Copyright
© Cambridge University Press, 2017 

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References

André, E., Klesen, M., Gebhard, P., Allen, S. & Rist, T. 2000. Integrating models of personality and emotions into lifelike characters. In Affective Interactions, A. Paiva (ed.), LNCS 1814, 150–165. Springer.Google Scholar
Axelrod, R. & Hamilton, W. D. 1981. The evolution of cooperation. Science 211(4489), 1390–1396.Google Scholar
Bloembergen, D., Ranjbar-Sahraei, B., Bou Ammar, H., Tuyls, K. & Weiss, G. 2014. Influencing social networks: an optimal control study. In Proceedings of ECAI’14, 105–110.Google Scholar
Collenette, J., Atkinson, K., Bloembergen, D. & Tuyls, K. 2016a. Modelling mood in co-operative emotional agents. In Proceedings of DARS'16.Google Scholar
Collenette, J., Atkinson, K., Bloembergen, D. & Tuyls, K. 2016b. Mobility effects on the evolution of co-operation in emotional robotic agents. In Proceedings of ALA Workshop.Google Scholar
Collenette, J., Atkinson, K., Bloembergen, D. & Tuyls, K. 2016c. The effect of mobility and emotion on interactions in multi-agent systems. In Proceedings of STAIRS’16.Google Scholar
Fehr, E. & Schmidt, K. M. 1999. A theory of fairness, competition, and cooperation. Quarterly Journal of Economics 114(3), 817–868.Google Scholar
Gerkey, B., Vaughan, R. T. & Howard, A. 2003. The player/stage project: tools for multi-robot and distributed sensor systems. In Proceedings of ICAR’03, 317–323.Google Scholar
Gibson, E. L. 2006. Emotional influences on food choice: sensory, physiological and psychological pathways. Physiology & Behavior 89(1), 53–61.Google Scholar
Gintis, H. 2000. Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Behavior. Princeton University Press.Google Scholar
Gray, E. K., Watson, D., Payne, R. & Cooper, C. 2001. Emotion, mood, and temperament: similarities, differences, and a synthesis. In Emotions at Work: Theory, Research and Applications for Management, Chapter 2, Payne, R. L., & Cooper, C. L. (eds). Wiley and Sons: Chichester, UK, 21–43.Google Scholar
Haley, W. E. & Strickland, B. R. 1986. Interpersonal betrayal and cooperation: effects on self-evaluation in depression. Journal of Personality and Social Psychology 50(2), 386–391.Google Scholar
Hertel, G., Neuhof, J., Theuer, T. & Kerr, N. L. 2000. Mood effects on cooperation in small groups: Does positive mood simply lead to more cooperation? Cognition & Emotion 14(4), 441–472.Google Scholar
Hilbe, C., Traulsen, A. & Sigmund, K. 2015. Partners or rivals? Strategies for the iterated prisoner’s dilemma. Games and Economic Behavior 92, 41–52.Google Scholar
Hofmann, L.-M., Chakraborty, N. & Sycara, K. 2011. The evolution of cooperation in self-interested agent societies: a critical study. In Proceedings of AAMAS, 685–692.Google Scholar
Keltner, D. & Gross, J. J. 1999. Functional accounts of emotions. Cognition & Emotion 13(5), 467–480.CrossRefGoogle Scholar
Leahy, R. L. 2005. Clinical implications in the treatment of mania: reducing risk behavior in manic patients. Cognitive and Behavioral Practice 12(1), 89–98.Google Scholar
Levenson, R. W. 1994. Human emotion: a functional view. The Nature of Emotion: Fundamental Questions 1, 123–126.Google Scholar
Lloyd-Kelly, M., Atkinson, K. & Bench-Capon, T. 2012a. Developing co-operation through simulated emotional behaviour. In 13th International Workshop on Multi-Agent Based Simulation.Google Scholar
Lloyd-Kelly, M., Atkinson, K. & Bench-Capon, T. 2012b. Emotion as an enabler of co-operation. In ICAART (2), 164–169.Google Scholar
Lloyd-Kelly, M., Atkinson, K. & Bench-Capon, T. 2014. Fostering co-operative behaviour through social intervention. In Proceedings of SIMULTECH’14, 578–585. IEEE.Google Scholar
Lount, R. B. J. 2010. The impact of positive mood on trust in interpersonal and intergroup interactions. Journal of Personality and Social Psychology 98(3), 420–433.Google Scholar
Ortony, A., Clore, G. L. & Collins, A. 1990. The Cognitive Structure of Emotions. Cambridge University Press.Google Scholar
Popescu, A., Broekens, J. & van Someren, M. 2014. Gamygdala: an emotion engine for games. IEEE Transactions on Affective Computing 5(1), 32–44.Google Scholar
Posner, J., Peterson, B. & Russell, J. 2005. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology 17(3), 715–734.Google Scholar
Ranjbar-Sahraei, B., Bou Ammar, H., Bloembergen, D., Tuyls, K. & Weiss, G. 2014a. Evolution of cooperation in arbitrary complex networks. In Proceedings of AAMAS’14, 677–684.Google Scholar
Ranjbar-Sahraei, B., Groothuis, I. M., Tuyls, K. & Weiss, G. 2014b. Valuation of cooperation and defection in small-world networks: a behavioral robotic approach. In Proceedings of BNAIC 2014.Google Scholar
Santos, F. C., Santos, M. D. & Pacheco, J. M. 2008. Social diversity promotes the emergence of cooperation in public goods games. Nature 454(7201), 213–216.Google Scholar
Schwarz, N. 2000. Emotion, cognition, and decision making. Cognition and Emotion 14(4), 433–440.CrossRefGoogle Scholar
Steunebrink, B. R., Dastani, M., Meyer, J.-J. C. 2007. A logic of emotions for intelligent agents. Proceedings of AAAI’07 22, 142.Google Scholar
Van Veelen, M., Garca, J., Rand, D. G. & Nowak, M. A. 2012. Direct reciprocity in structured populations. Proceedings of the National Academy of Sciences 109(25), 9929–9934.Google Scholar