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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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