A flexible and dynamic mobile robot localization approach

[EN] The main goal of this paper is to provide an approach to solve the problem of localization in mobile robots using multi-agent systems. Usually, the robot localization problem is solved in static environments by the addition of the needed sensors in order to help the robot, but this is not useful in dynamic environments where the robot is moving through different rooms or areas. The novelty of this dynamic scenario is that each room is composed of external devices that can enter or exit the system in a dynamic way and report the position where the robot is. In this way, we propose a multi-agent system using the SPADE multi-agent technology platform to improve the location of mobile robots in dynamic scenarios. To do this, we are going to use some of the advantages offered by the SPADE platform such as presence notification and subscription protocols in order to design a friendship network between sensors/devices and the mobile robots.This work was supported by the project TIN2015-65515-C4-1-R of the Spanish government.Peñaranda-Cebrián, C.; Palanca Cámara, J.; Julian Inglada, VJ.; Botti, V. (2018). A flexible and dynamic mobile robot localization approach. Logic Journal of IGPL. https://doi.org/10.1093/jigpal/jzy045

Mobile University Notification System Using Jabber Protocol

This progress report consists of sections, Introduction, Literature Review, Methodology, Results & Discussion and Conclusion. For the Introduction section includes the project's background, problem statement, objectives and scope of work. In the Literature Review section consist of results from literature gathering from various sources like articles, journals or the Internet. The section is divided to Introduction, Problem Statement, Current Implementation/Related Works and Support. For the Methodology section consist of the project's proposed methodology or how the project will be conducted. The project is using the Waterfall Methodology. For the Results & Discussion section consist of the previous activities conducted for the project and their end results. Finally the Conclusion section will include the final conclusion for this interim

Self-organizing multi-agent system for management and planning surveillance routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study.Web of Science3151100108

How to connect design thinking and cyber-physical systems: the s*IoT conceptual modelling approach

The alignment of enterprise models and information systems is a factor that influences the efficiency of enterprise practices. Considering the changing landscape in the age of the fourth industrial revolution, it is imperative that alignment methodologies are evolved with the progression of enterprise models and the transformation from information systems to cyber-physical systems (CPSs). This issue was dissected in three layers - scenario layer, modelling layer, and run-time environment. In this structure, design thinking and CPSs were extended from the scenario layer and the run-time environment to the modelling layer. Focusing on the modelling layer, progress was made towards composing smart models that innovate enterprise models according to novel influences from design thinking while abstracting from run-time environments that CPS provide. The hypothesis was to consider the automated transformation of knowledge as an axle around which artifacts on the modelling layer revolve. Based on this hypothesis, the modelling layer was structured in a modelling hierarchy, in which a metamodel was defined using a metamodelling platform. The metamodel is the direct model of modelling methods which were used to build smart models that connect design thinking and CPSs

MAIA: an event-based modular architecture for intelligent agents

Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic. behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform

Localization of charging stations for electric vehicles using genetic algorithms

[EN] The electric vehicle (EV) is gradually being introduced in cities. The impact of this introduction is less due, among other reasons, to the lack of charging infrastructure necessary to satisfy the demand. In today¿s cities there is no adequate infrastructure and it is necessary to have action plans that allow an easy deployment of a network of EV charging points in current cities. These action plans should try to place the EV charging stations in the most appropriate places for optimizing their use. According to this, this paper presents an agent-oriented approach that analyses the different configurations of possible locations of charging stations for the electric vehicles in a specific city. The proposed multi-agent system takes into account data from a variety of sources such as social networks activity and mobility information in order to estimate the best configurations. The proposed approach employs a genetic algorithm (GA) that tries to optimize the possible configurations of the charging infrastructure. Additionally, a new crossover method for the GA is proposed considering this context.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and MODINVECI project of the Spanish government. Vicent Botti and Jaume Jordan are funded by UPV PAID-06-18 project. Jaume Jordan is funded by grant APOSTD/2018/010 of GVA-FSEJordán, J.; Palanca Cámara, J.; Del Val Noguera, E.; Julian Inglada, VJ.; Botti, V. (2021). Localization of charging stations for electric vehicles using genetic algorithms. Neurocomputing. 452:416-423. https://doi.org/10.1016/j.neucom.2019.11.122S41642345

Self-Organizing Multi-Agent System for Management and Planning Surveillance Routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study

Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks

[EN] Nowadays there is a strong integration of online social platforms and applications with our daily life. Such interactions can make risks arise and compromise the information we share, thereby leading to privacy issues. In this work, a proposal that makes use of a software agent that performs sentiment analysis and another performing stress analysis on keystroke dynamics data has been designed and implemented. The proposal consists of a set of new agents that have been integrated into a multi-agent system (MAS) for guiding users interacting in online social environments, which has agents for sentiment and stress analysis on text. We propose a combined analysis using the different agents. The MAS analyzes the states of the users when they are interacting, and warns them if the messages they write are deemed negative. In this way, we aim to prevent potential negative outcomes on social network sites (SNSs). We performed experiments in the laboratory with our private SNS Pesedia over a period of one month, so we gathered data about text messages and keystroke dynamics data, and used the datasets to train the artificial neural networks (ANNs) of the agents. A set of experiments was performed for discovering which analysis is able to detect a state of the user that propagates more in the SNS, so it may be more informative for the MAS. Our study will help develop future intelligent systems that utilize user data in online social environments for guiding or helping them in their social experience.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks. Applied Sciences. 10(11):1-20. https://doi.org/10.3390/app10113754S1201011O’Keeffe, G. S., & Clarke-Pearson, K. (2011). The Impact of Social Media on Children, Adolescents, and Families. PEDIATRICS, 127(4), 800-804. doi:10.1542/peds.2011-0054George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Aguado, G., Julian, V., & Garcia-Fornes, A. (2018). Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis. Information, 9(5), 107. doi:10.3390/info9050107Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Huang, F., Zhang, X., Zhao, Z., Xu, J., & Li, Z. (2019). Image–text sentiment analysis via deep multimodal attentive fusion. Knowledge-Based Systems, 167, 26-37. doi:10.1016/j.knosys.2019.01.019Mehrabian, A. (1996). Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament. Current Psychology, 14(4), 261-292. doi:10.1007/bf02686918Ulinskas, M., Damaševičius, R., Maskeliūnas, R., & Woźniak, M. (2018). Recognition of human daytime fatigue using keystroke data. Procedia Computer Science, 130, 947-952. doi:10.1016/j.procs.2018.04.09

Organisational Metamodel for Large-Scale Multi-Agent Systems: First Steps Towards Modelling Organisation Dynamics

The research presented in this paper is a thesis proposal with the main goal of defining an ontology comprising chosen organisational concepts applicable to large-scale multiagent systems (LSMAS), and building a metamodel for modelling selected organisational features in such systems. The method of applying aspects of human organisations to multiagent systems (MAS) comprising autonomous intelligent agents will be enriched through this research with a new perspective of modelling organisation dynamics in LSMAS. Results of this research, in their final version, will be tested using testbed scenarios based on a specific massively multi-player online role-playing game (MMORPG), since MMORPGs are one of the identified application domains of LSMAS. It is important to note that results described in this paper showcase partial results in their early stage of development. Nevertheless, first traces of a modelling tool that is expected to aid in development of LSMAS for numerous application domains, and ease their organisational design, are recognisable in the proposed combination of ontology engineering, metamodelling and code generating methods

Taxi services and the carsharing alternative: a case study of Valencia city

[EN] The public's awareness of pollution in cities is growing. The decrease of carbon dioxide emissions from the use of fossil-fuel-powered cars stands out among the different viable alternatives. To this purpose, more sustainable options, such as carsharing fleets, could be used to replace private automobiles and other services such as taxis. This type of vehicle, which is usually electric, is becoming more common in cities, providing a green mobility option. In this research, we use multi-agent simulations to examine the efficiency of the current taxi fleet in Valencia. After that, we evaluate various carsharing fleet arrangements. Our findings demonstrate the possibility for a mix of the two types of fleets to meet present demand while also improving the city's sustainability.This work is partially supported by grant RTI2018-095390-B-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe". Pasqual Martí is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana". Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/ 10.13039/501100011033 and by "European Union NextGenerationEU/PRTR". Pablo Chamoso is supported by grant CCTT3/20/SA/0002 (AIR-SCity project), funded by Institute for Business Competitiveness of Castilla y León, and the European Regional Development Fund.Martí, P.; Jordán, J.; Chamoso, P.; Julian, V. (2022). Taxi services and the carsharing alternative: a case study of valencia city. Mathematical Biosciences and Engineering. 19(7):6680-6698. https://doi.org/10.3934/mbe.202231466806698197L. Rayle, D. Dai, N. Chan, R. Cervero, S. Shaheen, Just a better taxi? a survey-based comparison of taxis, transit, and ridesourcing services in san francisco, Transp. Policy, 45 (2016), 168–178. https://doi.org/10.1016/j.tranpol.2015.10.004R. Katzev, Car sharing: a new approach to urban transportation problems, Anal. Soc. Issues Public Policy, 3 (2003), 65–86. https://doi.org/10.1111/j.1530-2415.2003.00015.xM. Namazu, H. Dowlatabadi, Vehicle ownership reduction: a comparison of one-way and two-way carsharing systems, Transp. Policy, 64 (2018), 38–50. https://doi.org/10.1016/j.tranpol.2017.11.001A. Kolleck, Does car-sharing reduce car ownership? empirical evidence from Germany, Sustainability, 13 (2021), 7384. https://doi.org/10.3390/su13137384J. Firnkorn, M. Müller, What will be the environmental effects of new free-floating car-sharing systems? the case of car2go in Ulm, Ecol. Econ., 70 (2011), 1519–1528. https://doi.org/10.1016/j.ecolecon.2011.03.014X. Dong, Y. Cai, J. Cheng, B. Hu, H. Sun, Understanding the competitive advantages of car sharing from the travel-cost perspective, Int. J. Environ. Res. Public Health, 17 (2020), 4666. https://doi.org/10.3390/ijerph17134666T. Yoon, C. R. Cherry, M. S. Ryerson, J. E. Bell, Carsharing demand estimation and fleet simulation with EV adoption, J. Cleaner Prod., 206 (2019), 1051–1058. https://doi.org/10.1016/j.jclepro.2018.09.124J. Palanca, A. Terrasa, C. Carrascosa, V. Julián, Simfleet: a new transport fleet simulator based on MAS, in International Conference on Practical Applications of Agents and Multi-Agent Systems, (2019), 257–264. https://doi.org/10.1007/978-3-030-24299-2_22P. Martí, J. Jordán, V. Julián, Carsharing in valencia: analysing an alternative to taxi fleets, in Practical Applications of Agents and Multi-Agent Systems, Springer, (2021), 270–282. https://doi.org/10.1007/978-3-030-85710-3_23M. E. Gregori, J. P. Cámara, G. A. Bada, A jabber-based multi-agent system platform, in Proceedings of the Fifth International Joint Conference on Autonomous Aagents and Multiagent Systems, (2006), 1282–1284. https://doi.org/10.1145/1160633.1160866P. Martí, J. Jordán, J. Palanca, V. Julian, Free-floating carsharing in SimFleet, in International Conference on Intelligent Data Engineering and Automated Learning, Springer, (2020), 221–232. https://doi.org/10.1007/978-3-030-62362-3_20P. Martí, J. Jordán, J. Palanca, V. Julian, Load generators for automatic simulation of urban fleets, in International Conference on Practical Applications of Agents and Multi-Agent Systems, Springer, (2020), 394–405. https://doi.org/10.1007/978-3-030-51999-5_33N. Firdausiyah, E. Taniguchi, A. G. Qureshi, Modeling city logistics using adaptive dynamic programming based multi-agent simulation, Transp. Res. Part E: Logist. Transp. Rev., 125 (2019), 74–96. https://doi.org/10.1016/j.tre.2019.02.011C. Standing, F. Jie, T. Le, S. Standing, S. Biermann, Analysis of the use and perception of shared mobility: a case study in western Australia, Sustainability, 13 (2021), 8766. https://doi.org/10.3390/su13168766H. Qin, E. Su, Y. Wang, J. Li, Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems, Omega, 109 (2022), 102609. https://doi.org/10.1016/j.omega.2022.102609H. Habekotté, Optimizing Carsharing Policies for a New Generation-A Quest on How to Upscale Carsharing as Part of Sustainable Mobility Systems in Dutch Urban Regions, PhD thesis, University of Groningen, 2021.A. Ciociola, D. Markudova, L. Vassio, D. Giordano, M. Mellia, M. Meo, Impact of charging infrastructure and policies on electric car sharing systems, in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, (2020), 1–6. https://doi.org/10.1109/ITSC45102.2020.9294282J. Schlüter, A. Bossert, P. Rössy, M. Kersting, Impact assessment of autonomous demand responsive transport as a link between urban and rural areas, Res. Trans. Bus. Manage., 39 (2021), 100613. https://doi.org/10.1016/j.rtbm.2020.100613F. Javanshour, H. Dia, G. Duncan, R. Abduljabbar, S. Liyanage, Performance evaluation of station-based autonomous on-demand car-sharing systems, IEEE Trans. Intell. Transp. Syst., 2021 (2021), 1–12. https://doi.org/10.1109/TITS.2021.3071869P. Martí, J. Jordán, J. Palanca, V. Julian, Charging stations and mobility data generators for agent-based simulations, Neurocomputing, 484 (2022), 196–210. https://doi.org/10.1016/j.neucom.2021.06.098D. I. Grozev, D. E. Topchu, D. I. Miteva, Assessment of CO2 emissions released from the taxi vehicle fleet in Ruse, in Proceedings of the 2nd Virtual Multidisciplinary Conference, (2014), 484–487.J. Jordán, P. Martí, J. Palanca, V. Julian, V. Botti, Interurban electric vehicle charging stations through genetic algorithms, in International Conference on Hybrid Artificial Intelligence Systems, Springer, (2021), 101–112. https://doi.org/10.1007/978-3-030-86271-8_9J. Jordán, J. Palanca, E. del Val, V. Julian, V. Botti, Localization of charging stations for electric vehicles using genetic algorithms, Neurocomputing, 452 (2021), 416–423. https://doi.org/10.1016/j.neucom.2019.11.12