Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions.


Journal

Journal of healthcare engineering
ISSN: 2040-2309
Titre abrégé: J Healthc Eng
Pays: England
ID NLM: 101528166

Informations de publication

Date de publication:
2022
Historique:
received: 08 10 2021
accepted: 28 12 2021
entrez: 7 2 2022
pubmed: 8 2 2022
medline: 3 5 2022
Statut: epublish

Résumé

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.

Identifiants

pubmed: 35126919
doi: 10.1155/2022/3449433
pmc: PMC8808197
doi:

Substances chimiques

Neurotransmitter Agents 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3449433

Informations de copyright

Copyright © 2022 Awais Hassan et al.

Déclaration de conflit d'intérêts

The authors declare that they have no conflicts of interest.

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Auteurs

Awais Hassan (A)

Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan.

Maida Shahid (M)

Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan.

Faisal Hayat (F)

Department of Computer Engineering, University of Engineering and Technology, Lahore 54890, Pakistan.

Jehangir Arshad (J)

Department of Electrical & Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.

Mujtaba Hussain Jaffery (MH)

Department of Electrical & Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.

Ateeq Ur Rehman (AU)

Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan.

Kalim Ullah (K)

Department of Zoology, Kohat University of Science and Technology, Kohat, Khyber Pakhtunkhwa, Pakistan.

Seada Hussen (S)

School of Electrical and Computer Engineering, Haramaya Institute of Technology, 138 Diredawa, Ethiopia.

Habib Hamam (H)

Faculty of Engineering, Uni de Moncton, Moncton NB E1A3E9, Canada.
Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia.
School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.

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