Cargando…

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

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 t...

Descripción completa

Detalles Bibliográficos
Autores principales: Hassan, Awais, Shahid, Maida, Hayat, Faisal, Arshad, Jehangir, Jaffery, Mujtaba Hussain, Rehman, Ateeq Ur, Ullah, Kalim, Hussen, Seada, Hamam, Habib
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808197/
https://www.ncbi.nlm.nih.gov/pubmed/35126919
http://dx.doi.org/10.1155/2022/3449433
_version_ 1784643833566330880
author Hassan, Awais
Shahid, Maida
Hayat, Faisal
Arshad, Jehangir
Jaffery, Mujtaba Hussain
Rehman, Ateeq Ur
Ullah, Kalim
Hussen, Seada
Hamam, Habib
author_facet Hassan, Awais
Shahid, Maida
Hayat, Faisal
Arshad, Jehangir
Jaffery, Mujtaba Hussain
Rehman, Ateeq Ur
Ullah, Kalim
Hussen, Seada
Hamam, Habib
author_sort Hassan, Awais
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8808197
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-88081972022-02-03 Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions Hassan, Awais Shahid, Maida Hayat, Faisal Arshad, Jehangir Jaffery, Mujtaba Hussain Rehman, Ateeq Ur Ullah, Kalim Hussen, Seada Hamam, Habib J Healthc Eng Research Article 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. Hindawi 2022-01-25 /pmc/articles/PMC8808197/ /pubmed/35126919 http://dx.doi.org/10.1155/2022/3449433 Text en Copyright © 2022 Awais Hassan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hassan, Awais
Shahid, Maida
Hayat, Faisal
Arshad, Jehangir
Jaffery, Mujtaba Hussain
Rehman, Ateeq Ur
Ullah, Kalim
Hussen, Seada
Hamam, Habib
Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions
title Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions
title_full Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions
title_fullStr Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions
title_full_unstemmed Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions
title_short Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions
title_sort improving the survival time of multiagents in social dilemmas through neurotransmitter-based deep q-learning model of emotions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808197/
https://www.ncbi.nlm.nih.gov/pubmed/35126919
http://dx.doi.org/10.1155/2022/3449433
work_keys_str_mv AT hassanawais improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT shahidmaida improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT hayatfaisal improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT arshadjehangir improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT jafferymujtabahussain improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT rehmanateequr improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT ullahkalim improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT hussenseada improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions
AT hamamhabib improvingthesurvivaltimeofmultiagentsinsocialdilemmasthroughneurotransmitterbaseddeepqlearningmodelofemotions