Meta-razonamiento en Agentes con Restricciones Temporales Críticas

El paradigma de agentes/sistemas multi-agente es uno de lo smodelos computacionales de mayor relevancia de los últimos tiempos, habiendo dado lugar a múltiples investigaciones y aplicaciones concretas. Este modelo computacional tiene como objetivo la construcción de sistemas que se enfrenten a situaciones mostrando ciertas características propias de un ser humano, tales como inteligencia, reactividad, pro-actividad,... De entre todas las variedades de tipos de agente que se pueden definir, el trabajo realizado se centra en aquellos agentes que deben trabajar en un entorno con restricciones temporales críticas, es decir, donde existen ciertos problemas a los que el agente debe dar respuesta antes de que pase un determinado tiempo o las consecuencias serán catastróficas. En un agente de este tipo es fundamental tratar de conseguir un uso óptimo del tiempo de procesador, recurso más importante en esta clase de sistemas. Es por esto que resulta relevante ocnseguir que dicho agente sea capaz de dedicar su timpo de procesador a aquello que sea necesario de acuerdo a la situación en la que se encuentre. Para conseguir esta adaptación es fundamental que el agente sea capaz de razonar sobre su propio proceso de razonamiento, es decir, meta-razonar, siempre teniendo en cuenta que este proceso de metarazonamiento va a consumir también tiempo de procesador. De esta manera, el objetivo de este trabajo es el estudio de las capacidades necesarias para poder incorporar la habilidad de meta-razonar a un agente con restricciones temporales críticas, así como la incorporación a una arquitectura de agente concreta, la de agente ARTIS. Después del estudio comentado, se llegó a la conclusión de que para poder incorporar la habilidad de meta-razonamiento a un agente con restricciones temporales críticas era necesario incluir al agente las siguientes capacidades: detección de situaciones significativas, adaptar su comportamiento, adaptar el proceso de razonamiento del agente teniendo en ..Carrascosa Casamayor, C. (2004). Meta-razonamiento en Agentes con Restricciones Temporales Críticas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2670Palanci

Supportive consensus

[EN] The paper is concerned with the consensus problem in a multi-agent system such that each agent has boundary constraints. Classical Olfati-Saber's consensus algorithm converges to the same value of the consensus variable, and all the agents reach the same value. These algorithms find an equality solution. However, what happens when this equality solution is out of the range of some of the agents? In this case, this solution is not adequate for the proposed problem. In this paper, we propose a new kind of algorithms called supportive consensus where some agents of the network can compensate for the lack of capacity of other agents to reach the average value, and so obtain an acceptable solution for the proposed problem. Supportive consensus finds an equity solution. In the rest of the paper, we define the supportive consensus, analyze and demonstrate the network's capacity to compensate out of boundaries agents, propose different supportive consensus algorithms, and finally, provide some simulations to show the performance of the proposed algorithms.The author(s) received specific funding for this work from the Valencian Research Institute for Artificial Intelligence (VRAIN) where the authors are currently working. This work is partially supported by the Spanish Government project RTI2018-095390-B-C31, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Palomares Chust, A.; Rebollo Pedruelo, M.; Carrascosa Casamayor, C. (2020). Supportive consensus. PLoS ONE. 15(12):1-30. https://doi.org/10.1371/journal.pone.0243215S1301512Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and Cooperation in Networked Multi-Agent Systems. Proceedings of the IEEE, 95(1), 215-233. doi:10.1109/jproc.2006.887293Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y. C., Chiclana, F., & Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459, 20-35. doi:10.1016/j.ins.2018.05.017Fischbacher, U., & Gächter, S. (2010). Social Preferences, Beliefs, and the Dynamics of Free Riding in Public Goods Experiments. American Economic Review, 100(1), 541-556. doi:10.1257/aer.100.1.541Du, S., Hu, L., & Song, M. (2016). Production optimization considering environmental performance and preference in the cap-and-trade system. Journal of Cleaner Production, 112, 1600-1607. doi:10.1016/j.jclepro.2014.08.086Alfonso, B., Botti, V., Garrido, A., & Giret, A. (2013). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers, 16(2), 183-199. doi:10.1007/s10796-013-9443-8Rebollo M, Carrascosa C, Palomares A. Consensus in Smart Grids for Decentralized Energy Management. In: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. Springer; 2014. p. 250–261.Zhao, T., & Ding, Z. (2018). Distributed Agent Consensus-Based Optimal Resource Management for Microgrids. IEEE Transactions on Sustainable Energy, 9(1), 443-452. doi:10.1109/tste.2017.2740833Qiu, Z., Liu, S., & Xie, L. (2018). Necessary and sufficient conditions for distributed constrained optimal consensus under bounded input. International Journal of Robust and Nonlinear Control, 28(6), 2619-2635. doi:10.1002/rnc.4040Wei Ren, & Beard, R. W. (2005). Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 50(5), 655-661. doi:10.1109/tac.2005.846556Ren, W., & Beard, R. W. (2008). Distributed Consensus in Multi-vehicle Cooperative Control. Communications and Control Engineering. doi:10.1007/978-1-84800-015-5Knorn F, Corless MJ, Shorten RN. A result on implicit consensus with application to emissions control. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference; 2011. p. 1299–1304.Roy, S. (2015). Scaled consensus. Automatica, 51, 259-262. doi:10.1016/j.automatica.2014.10.073Mo, L., & Lin, P. (2018). Distributed consensus of second-order multiagent systems with nonconvex input constraints. International Journal of Robust and Nonlinear Control, 28(11), 3657-3664. doi:10.1002/rnc.4076Wang, Q., Gao, H., Alsaadi, F., & Hayat, T. (2014). An overview of consensus problems in constrained multi-agent coordination. Systems Science & Control Engineering, 2(1), 275-284. doi:10.1080/21642583.2014.897658Xi, J., Yang, J., Liu, H., & Zheng, T. (2018). Adaptive guaranteed-performance consensus design for high-order multiagent systems. Information Sciences, 467, 1-14. doi:10.1016/j.ins.2018.07.069Fontan A, Shi G, Hu X, Altafini C. Interval consensus: A novel class of constrained consensus problems for multiagent networks. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC); 2017. p. 4155–4160.Hou, W., Wu, Z., Fu, M., & Zhang, H. (2018). Constrained consensus of discrete-time multi-agent systems with time delay. International Journal of Systems Science, 49(5), 947-953. doi:10.1080/00207721.2018.1433899Elhage N, Beal J. Laplacian-based consensus on spatial computers. In: AAMAS; 2010. p. 907–914.Cavalcante R, Rogers A, Jennings N. Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method. In: Proc. of AAMAS’11; 2011. p. 165–172.Hu, H., Yu, L., Zhang, W.-A., & Song, H. (2013). Group consensus in multi-agent systems with hybrid protocol. Journal of the Franklin Institute, 350(3), 575-597. doi:10.1016/j.jfranklin.2012.12.020Ji, Z., Lin, H., & Yu, H. (2012). Leaders in multi-agent controllability under consensus algorithm and tree topology. Systems & Control Letters, 61(9), 918-925. doi:10.1016/j.sysconle.2012.06.003Li, Y., & Tan, C. (2019). A survey of the consensus for multi-agent systems. Systems Science & Control Engineering, 7(1), 468-482. doi:10.1080/21642583.2019.1695689Salazar, N., Rodriguez-Aguilar, J. A., & Arcos, J. L. (2010). Robust coordination in large convention spaces. AI Communications, 23(4), 357-372. doi:10.3233/aic-2010-0479Pedroche F, Rebollo M, Carrascosa C, Palomares A. On the convergence of weighted-average consensus. CoRR. 2013;abs/1307.7562

FLaMAS: Federated Learning Based on a SPADE MAS

[EN] In recent years federated learning has emerged as a new paradigm for training machine learning models oriented to distributed systems. The main idea is that each node of a distributed system independently trains a model and shares only model parameters, such as weights, and does not share the training data set, which favors aspects such as security and privacy. Subsequently, and in a centralized way, a collective model is built that gathers all the information provided by all of the participating nodes. Several federated learning framework proposals have been developed that seek to optimize any aspect of the learning process. However, a lack of flexibility and dynamism is evident in many cases. In this regard, this study aims to provide flexibility and dynamism to the federated learning process. The methodology used consists of designing a multi-agent system that can form a federated learning framework where the agents act as nodes that can be easily added to the system dynamically. The proposal has been evaluated with different experiments on the SPADE platform; the results obtained demonstrate the benefits of the federated system while facilitating flexibility and scalability.This research was partially supported by the MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government.Rincón-Arango, JA.; Julian, V.; Carrascosa Casamayor, C. (2022). FLaMAS: Federated Learning Based on a SPADE MAS. Applied Sciences. 12(7):1-14. https://doi.org/10.3390/app1207370111412

Agent Bodies: An Interface Between Agent and Environment

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23850-0_2Interfacing the agents with their environment is a classical problem when designing multiagent systems. However, the models pertaining to this interface generally choose to either embed it in the agents, or in the environment. In this position paper, we propose to highlight the role of agent bodies as primary components of the multiagent system design. We propose a tentative definition of an agent body, and discuss its responsibilities in terms of MAS components. The agent body takes from both agent and environment: low-level agent mechanisms such as perception and influences are treated locally in the agent bodies. These mechanism participate in the cognitive process, but are not driven by symbol manipulation. Furthermore, it allows to define several bodies for one mind, either to simulate different capabilities, or to interact in the different environments - physical, social- the agent is immersed in. We also draw the main challenges to apply this concept effectively.Saunier, J.; Carrascosa Casamayor, C.; Galland, S.; Kanmeugne, PS. (2015). Agent Bodies: An Interface Between Agent and Environment. En Agent Environments for Multi-Agent Systems IV. 4th International Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May 6, 2014. 25-40. doi:10.1007/978-3-319-23850-0_2S2540Barella, A., Ricci, A., Boissier, O., Carrascosa, C.: MAM5: Multi-agent model for intelligent virtual environments. In: 10th European Workshop on Multi-Agent Systems (EUMAS 2012), pp. 16–30 (2012)Behe, F., Galland, S., Gaud, N., Nicolle, C., Koukam, A.: An ontology-based metamodel for multiagent-based simulations. Int. J. Simul. Model. Pract. Theor. 40, 64–85 (2014). http://authors.elsevier.com/sd/article/S1569190X13001342Brooks, R.A.: Intelligence without representation. Artif. Intell. 47(1), 139–159 (1991)Campos, J., López-Sánchez, M., Rodríguez-Aguilar, J.A., Esteva, M.: Formalising situatedness and adaptation in electronic institutions. In: Hübner, J.F., Matson, E., Boissier, O., Dignum, V. (eds.) COIN 2008. LNCS, vol. 5428, pp. 126–139. Springer, Heidelberg (2009)Galland, S., Balbo, F., Gaud, N., Rodriguez, S., Picard, G., Boissier, O.: Contextualize agent interactions by combining social and physical dimensions in the environment. In: Demazeau, Y., Decker, K. (eds.) 13th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), June 2015Galland, S., Balbo, F., Gaud, N., Rodriguez, S., Picard, G., Boissier, O.: A multidimensional environment implementation for enhancing agent interaction. In: Bordini, R., Elkind, E. (eds.) Autonomous Agents and Multiagent Systems (AAMAS 2015), Istanbul, Turkey, May 2015Galland, S., Gaud, N., Demange, J., Koukam, A.: Environment model for multiagent-based simulation of 3D urban systems. In: the 7th European Workshop on Multiagent Systems (EUMAS 2009), Ayia Napa, Cyprus, December 2009 (paper 36)Gechter, F., Contet, J.M., Lamotte, O., Galland, S., Koukam, A.: Virtual intelligent vehicle urban simulator: application to vehicle platoon evaluation. Simul. Model. Practice Theor. (SIMPAT) 24, 103–114 (2012)Gibson, J.J.: The Theory of Affordances. Hilldale, USA (1977)Gouaïch, A., Michel, F., Guiraud, Y.: MIC $$^{*}$$ : a deployment environment for autonomous agents. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 109–126. Springer, Heidelberg (2005)Gouaïch, A., Michel, F.: Towards a unified view of the environment (s) within multi-agent systems. Informatica (Slovenia) 29(4), 423–432 (2005)Helleboogh, A., Vizzari, G., Uhrmacher, A., Michel, F.: Modeling dynamic environments in multiagent simulation. Int. J. Auton. Agents Multiagent Syst. 14(1), 87–116 (2007)Ketenci, U.G., Bremond, R., Auberlet, J.M., Grislin, E.: Drivers with limited perception: models and applications to traffic simulation. Recherche transports sécurité, RTS (2013)Michel, F.: The IRM4S model: the influence/reaction principle for multiagent based simulation. ACM, May 2007Okuyama, F.Y., Bordini, R.H., da Rocha Costa, A.C.: ELMS: an environment description language for multi-agent simulation. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 67–83. Springer, Heidelberg (2005)Platon, E., Sabouret, N., Honiden, S.: Environmental support for tag interactions. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 106–123. Springer, Heidelberg (2007)Ribeiro, T., Vala, M., Paiva, A.: Censys: a model for distributed embodied cognition. In: Aylett, R., Krenn, B., Pelachaud, C., Shimodaira, H. (eds.) IVA 2013. LNCS, vol. 8108, pp. 58–67. Springer, Heidelberg (2013)Ricci, A., Viroli, M., Omicini, A.: Programming MAS with artifacts. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) PROMAS 2005. LNCS (LNAI), vol. 3862, pp. 206–221. Springer, Heidelberg (2006)Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive stigmergy: towards a framework based on agents and artifacts. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 124–140. Springer, Heidelberg (2007)Ricci, A., Piunti, M., Viroli, M.: Environment programming in multi-agent systems: an artifact-based perspective. Auton. Agent. Multi-Agent Syst. 23(2), 158–192 (2011)Ricci, A., Viroli, M., Omicini, A.: Environment-based coordination through coordination artifacts. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 190–214. Springer, Heidelberg (2005)Ricci, A., Viroli, M., Omicini, A.: $${\sf CArtA gO}$$ : a framework for prototyping artifact-based environments in MAS. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 67–86. Springer, Heidelberg (2007)Rincon, J.A., Garcia, E., Julian, V., Carrascosa, C.: Developing adaptive agents situated in intelligent virtual environments. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 98–109. Springer, Heidelberg (2014)Saunier, J., Balbo, F., Pinson, S.: A formal model of communication and context awareness in multiagent systems. J. Logic Lang. Inform. 23(2), 219–247 (2014). http://dx.doi.org/10.1007/s10849-014-9198-8Saunier, J., Jones, H.: Mixed agent/social dynamics for emotion computation. In: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, pp. 645–652. International Foundation for Autonomous Agents and Multiagent Systems (2014)Simonin, O., Ferber, J.: Modeling self satisfaction and altruism to handle action selection and reactive cooperation. In: 6th International Conference on the Simulation of Adaptive Behavior (SAB 2000 volume 2), pp. 314–323 (2000)Thalmann, D., Musse, S.R.: Crowd Simulation. Springer, London (2007)Thiebaux, M., Marsella, S., Marshall, A., Kallmann, M.: Smartbody: Behavior realization for embodied conversational agents. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, vol. 1, pp. 151–158 (2008)Viroli, M., Holvoet, T., Ricci, A., Schelfthout, K., Zambonelli, F.: Infrastructures for the environment of multiagent system. Int. J. Auton. Agent. 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Developing an emotional-based application for human-agent societies

The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-016-2289-5The purpose of this paper is to present an emotional-based application for human-agent societies. This kind of applications are those where virtual agents and humans coexist and interact transparently into a fully integrated environment. Specifically, the paper presents an application where humans are immersed into a system that extracts and analyzes the emotional states of a human group trying to maximize the welfare of those humans by playing the most appropriate music in every moment. This system can be used not only online, calculating the emotional reaction of people in a bar to a new song, but also in simulation, to predict the people s reaction to changes in music or in the bar layout.This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI Grant AP2013-01276 awarded to Jaime-Andres Rincon.Rincón Arango, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2016). Developing an emotional-based application for human-agent societies. Soft Computing. 20(11):4217-4228. https://doi.org/10.1007/s00500-016-2289-5S421742282011Ali F, Amin M (2013) The influence of physical environment on emotions, customer satisfaction and behavioural intentions in chinese resort hotel industry. In: KMITL-AGBA conference Bangkok, pp 15–17Barella A, Ricci A, Boissier O, Carrascosa C (2012) MAM5: Multi-agent model for intelligent virtual environments. In: 10th European workshop on multi-agent systems (EUMAS 2012), pp 16–30Becker-Asano C, Wachsmuth I (2010) Affective computing with primary and secondary emotions in a virtual human. Auton Agents Multi-Agent Syst 20(1):32–49. doi: 10.1007/s10458-009-9094-9Billhardt H, Julián V, Corchado J, Fernández A (2014) An architecture proposal for human-agent societies. In: Highlights of practical applications of heterogeneous multi-agent systems. 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IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks

[EN] Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31), Universitat Politecnica de Valencia Research Grant PAID-10-19. S.G-O has been funded by grant PDBCEx COLDOC 679, scholarship programme from COLCIENCIAS (Administrative Department of Science, Technology and Innovation of Colombia).Rincón-Arango, JA.; Guerra-Ojeda, S.; Carrascosa Casamayor, C.; Julian, V. (2020). IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks. Sensors. 20(24):1-19. https://doi.org/10.3390/s20247353119202

Introducing dynamism in emotional agent societies

[EN] This paper presents the development of a dynamic emotional model to be employed in agent societies. The proposed model is based on the PAD emotional model and allows the representation of the emotional contagion phenomena of a heterogeneous group of agents that are capable of express emotions. The model is mainly based on three elements: personality, empathy and affinity. These elements allow the characterisation of each individual, causing them susceptible to vary in some degree the emotions of other individuals. Additionally, the model allows defining of the social emotion of this group of agents. (C) 2017 Elsevier B.V. All rights reserved.This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon. This work is supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the projects UID/CEC/00319/2013 and Post-Doc scholarship SFRH/BPD/102696/2014 (A. Costa).Rincon, J.; Costa, A.; Villarrubia, G.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2018). Introducing dynamism in emotional agent societies. Neurocomputing. 272:27-39. https://doi.org/10.1016/j.neucom.2017.03.091S273927

ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment

[EN] Recent studies show that the elderly population has increased considerably in European society in recent years. This fact has led the European Union and many countries to propose new policies for caring services directed to this group. The current trend is to promote the care of the elderly in their own homes, thus avoiding inverting resources on residences. With this in mind, there are now new solutions in this direction, which try to make use of the continuous advances in computer science. This paper tries to advance in this area by proposing the use of a personal assistant to help older people at home while carrying out their daily activities. The proposed personal assistant is called ME(3)CA, and can be described as a cognitive assistant that offers users a personalised exercise plan for their rehabilitation. The system consists of a sensorisation platform along with decision-making algorithms paired with emotion detection models. ME(3)CA detects the users' emotions, which are used in the decision-making process allowing for more precise suggestions and an accurate (and unbiased) knowledge about the users' opinion towards each exercise.This work was partly supported by the FCT-Fundacao para a Ciencia e Tecnologia through the Post-Doc scholarship SFRH/BPD/102696/2014 (A. Costa), the Generalitat Valenciana (PROMETEO/2018/002) and the Spanish Government (RTI2018-095390-B-C31).Rincon, J.; Araujo, A.; Novais, P.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2020). ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment. Sensors. 20(3):1-14. https://doi.org/10.3390/s20030852S11420

SPADE 3: Supporting the New Generation of Multi-Agent Systems

[EN] Although intelligent agent-based systems have existed for several years, the progression in terms of real applications or their integration in the industry have not yet reached the expected levels. During the last two decades, many agent platforms have appeared with the aim of simplifying the development of multi-agent systems. Some of these platforms have been designed for general purposes, while others have been oriented towards specific domains. However, the lack of standards and the complexity associated with supporting such systems, among other difficulties, have hampered their generalised use. This article looks in depth at the current situation of existing agent platforms, trying to analyse their current shortcomings and their expected needs in the near future. The goal of the paper is to identify possible lines of work and some of the most crucial aspects to be considered in order to popularize the application of agent technology as a dynamic and flexible solution to current problems. Moreover, the paper presents SPADE 3, a new version of the SPADE middleware, which has been totally redesigned in order to conform to the identified challenges. Finally, a case study is proposed to illustrate how SPADE 3 is able to fulfill these challenges.This work was supported in part by the Spanish Government, under Project RTI2018-095390-B-C31-AR.Palanca Cámara, J.; Terrasa Barrena, AM.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2020). SPADE 3: Supporting the New Generation of Multi-Agent Systems. IEEE Access. 8:182537-182549. https://doi.org/10.1109/ACCESS.2020.3027357S182537182549

Influencing over people with a social emotional model

[EN] This paper presents an approach of a social emotional model, which allows to extract the social emotion of a group of intelligent entities. The emotional model PAD allows to represent the emotion of an intelligent entity in 3-D space, allowing the representation of different emotional states. The social emotional model presented in this paper uses individual emotions of each one of the entities, which are represented in the emotional space PAD. Using a social emotional model within intelligent entities allows the creation of more real simulations, in which emotional states can influence decision-making. The result of this social emotional mode is represented by a series of examples, which are intended to represent a number of situations in which the emotions of each individual modify the emotion of the group. Moreover, the paper introduces an example which employs the proposed model in order to learn and predict future actions trying to influence in the social emotion of a group of people.This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon.Rincon, J.; De La Prieta-Pintado, F.; Zanardini, D.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2017). Influencing over people with a social emotional model. Neurocomputing. 231:47-54. https://doi.org/10.1016/j.neucom.2016.03.107S475423