Analysis of the emotions in a multi-robot system in emergent contexts
In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universa...
- Autores:
-
Gil, Angel
Puerto Cuadros, Eduard Gilberto
Aguilar, Jose
Dapena, Eladio
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/1562
- Acceso en línea:
- http://repositorio.ufps.edu.co/handle/ufps/1562
https://doi.org/10.1080/01969722.2020.1854420
- Palabra clave:
- Emergent systems
emotion
multi-robot systems
pattern recognition
- Rights
- openAccess
- License
- Copyright © 2021 Informa UK Limited
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Analysis of the emotions in a multi-robot system in emergent contexts |
| title |
Analysis of the emotions in a multi-robot system in emergent contexts |
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Analysis of the emotions in a multi-robot system in emergent contexts Emergent systems emotion multi-robot systems pattern recognition |
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Analysis of the emotions in a multi-robot system in emergent contexts |
| title_full |
Analysis of the emotions in a multi-robot system in emergent contexts |
| title_fullStr |
Analysis of the emotions in a multi-robot system in emergent contexts |
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Analysis of the emotions in a multi-robot system in emergent contexts |
| title_sort |
Analysis of the emotions in a multi-robot system in emergent contexts |
| dc.creator.fl_str_mv |
Gil, Angel Puerto Cuadros, Eduard Gilberto Aguilar, Jose Dapena, Eladio |
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Gil, Angel Puerto Cuadros, Eduard Gilberto Aguilar, Jose Dapena, Eladio |
| dc.subject.proposal.eng.fl_str_mv |
Emergent systems emotion multi-robot systems pattern recognition |
| topic |
Emergent systems emotion multi-robot systems pattern recognition |
| description |
In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universal emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions (anger, sadness or disgust). The more positive the robot is in its emotion, the more it will consider the group and global goals (joy). Each robot is able to recognize another robot′s emotion in the system based on their current state, using the AR2P (AR2P for its acronym in Spanish: Algoritmo Recursivo de Reconocimiento de Patrones) recognition algorithm. In this way, it can use this information of the emotions to decide with whom collaborate. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper explores the emerging behavior in two multi-robot scenarios; nectar harvesting and object transportation. The results show that the emotions are important to the emergent behavior in a multi-robot system. |
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2021 |
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2021-11-30T14:47:19Z |
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2021-01-12 |
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Cybernetics and Systems |
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Vol.52 No.4.(2021) |
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273 |
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Gil, A., Puerto, E., Aguilar, J., & Dapena, E. (2020). Analysis of the Emotions in a Multi-Robot System in Emergent Contexts. Cybernetics and Systems, 52(4), 245-273. |
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Cybernetics and Systems |
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Cybernetics and Systems |
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Gil, Angelbd52644340a9d157913ef2d192c3fcf7600Puerto Cuadros, Eduard Gilbertoc201373dd70e4d814c3342495d100a21600Aguilar, Jose50ad5b371a57d0bc8149a95c0f62781c600Dapena, Eladio519fe143ba6881d4a0b675dff2d4c5246002021-11-30T14:47:19Z2021-11-30T14:47:19Z2021-01-12http://repositorio.ufps.edu.co/handle/ufps/1562https://doi.org/10.1080/01969722.2020.1854420In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universal emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions (anger, sadness or disgust). The more positive the robot is in its emotion, the more it will consider the group and global goals (joy). Each robot is able to recognize another robot′s emotion in the system based on their current state, using the AR2P (AR2P for its acronym in Spanish: Algoritmo Recursivo de Reconocimiento de Patrones) recognition algorithm. In this way, it can use this information of the emotions to decide with whom collaborate. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper explores the emerging behavior in two multi-robot scenarios; nectar harvesting and object transportation. The results show that the emotions are important to the emergent behavior in a multi-robot system.29 páginasapplication/pdfengCybernetics and SystemsReino UnidoCybernetics and SystemsVol.52 No.4.(2021)2734(2021)24552Gil, A., Puerto, E., Aguilar, J., & Dapena, E. (2020). Analysis of the Emotions in a Multi-Robot System in Emergent Contexts. Cybernetics and Systems, 52(4), 245-273.Cybernetics and SystemsCopyright © 2021 Informa UK Limitedinfo:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://www.tandfonline.com/doi/full/10.1080/01969722.2020.1854420Analysis of the emotions in a multi-robot system in emergent contextsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Emergent systemsemotionmulti-robot systemspattern recognitionAguilar, J. 1998. Definition of an energy function for the random neural to solve optimization problems. Neural Networks 11:731–7.Aguilar, J. 2001. A fuzzy cognitive map based on the random neural model. Vol. 2072: Lecture notes in artificial intelligence, 333–8. Berlin: Springer-Verlag.Aguilar, J. 2013. Different dynamic causal relationship approaches for cognitive maps. Applied Soft Computing 13 (1):271–82. doi:10.1016/j.asoc.2012.08.037.Aguilar, J. 2014. Introducción a los Sistemas Emergentes. Mérida, Venezuela: Talleres Gráficos, Universidad de Los Andes.Alexander, J., and S. Smales. 1997. Intelligence, learning and long-term memory. Personality and Individual Differences 23 (5):815–25. doi:10.1016/S0191-8869(97)00054-8.Banik, S. C., K. Watanabe, M. K. Habib, and K. Izumi. 2008. An emotion-based task sharing approach for a cooperative multiagent robotic system. In Proceedings of the IEEE International Conference on Mechatronics Automation, 77–82. Takamatsu, Japan: IEEE.Banik, S. C., K. Watanabe, and K. Izumi. 2008. Improvement of group performance of job distributed mobile robots by an emotionally biased control system. Artificial Life and Robotics (12):245–49.Banik, S. C., K. Watanabe, and K. Izumi. 2007. Task allocation with a cooperative plan for an emotionally intelligent system of multi-robots. In Proceedings of the 46th Annual Conference of the Society of Instrument and Control Engineers of Japan, 1004–10. Takamatsu, Japan: IEEE.Botzheim, J., and N. Kubota. 2014. Spiking neural network based emotional model for robot partner. In IEEE Symposium on Robotic Intelligence in Informationally Structured Space (RiiSS), 1–6. Orlando, FL: IEEE.Cao, Y., A. Fukunaga, and F. Meng. 1995. Cooperative mobile robotics: antecedents and directions. In Proceedings of the Intelligent Robots and Systems, vol. 1, 226–34. Pittsburgh, PA: IEEE.Contreras, J., and J. Aguilar. 2010. The FCM designer tool. In Fuzzy cognitive maps: Advances in theory, methodologies, tools and application, ed. M. Glykas, 71–88. New York: Springer.De la Rosa, F., and M. E. Jiménez. 2009. Simulation of multi-robot architectures in mobile robotics. In IEEE Electronics, Robotics and Automotive Mechanics Conference, 199–203. Cuernavaca, Mexico: IEEE.Fang, B., L. Chen, H. Wang, S. Dai, and Q. Zhong. 2014. Research on Multirobot pursuit task allocation algorithm based on emotional cooperation factor. The Scientific World Journal 2014:1–6. doi:10.1155/2014/864180.Fernández, N., J. Aguilar, C. Piña-García, and C. Gershenson. 2017. Complexity of lakes in a latitudinal gradient. Ecological Complexity 31:1–20. doi:10.1016/j.ecocom.2017.02.002.Gautam, A., and S. Mohan. 2012. A review of research in multi-robot systems. In IEEE 7th International Conference on Industrial and Information Systems (ICIIS), 1–5. Chennai, India: IEEE.Gil, A., J. Aguilar, E. Dapena, and R. Rivas. 2020. Emotional model for a multi-robot system with emergent behavior. International Journal of Robotics and Automation 9 (3):220–32.Gil, A., J. Aguilar, R. Rivas, and E. Dapena. 2016. Módulo conductual inmerso en una arquitectura de control para sistemas multi-robots. Revista Ingeniería al Día 2 (1):40–57.Gil, A., J. Aguilar, R. Rivas, and E. 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
0000-0001-9361-5837c201373dd70e4d814c3342495d100a216000000-0003-4194-688250ad5b371a57d0bc8149a95c0f62781c6000000-0002-9135-0967519fe143ba6881d4a0b675dff2d4c524600 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