Case-based argumentation infrastructure for agent societies

In this work, we propose an infrastructure to develop and execute argumentative agents in an open multi-agent system. This infrastructure offers the necessary components to develop agents with argumentation capabilities, including the communication skills and the argumentation protocol, and it offers support for agent societies and their agents' social context. The main advantage of having this infrastructure is that it is possible to create agents with argumentation capabilities to resolve a specified problem. In the argumentation dialogue the agents try to reach an agreement about the best solution to apply for each proposed problem. The proposed infrastructure has been validated with a real example and it has been evaluated obtaining, with argumentation strategies, better performance than other reasoning approaches that do not include argumentation.Jordán Prunera, JM. (2011). Case-based argumentation infrastructure for agent societies. http://hdl.handle.net/10251/15362Archivo delegad

Non-Cooperative Games for Self-Interested Planning Agents

Multi-Agent Planning (MAP) is a topic of growing interest that deals with the problem of automated planning in domains where multiple agents plan and act together in a shared environment. In most cases, agents in MAP are cooperative (altruistic) and work together towards a collaborative solution. However, when rational self-interested agents are involved in a MAP task, the ultimate objective is to find a joint plan that accomplishes the agents' local tasks while satisfying their private interests. Among the MAP scenarios that involve self-interested agents, non-cooperative MAP refers to problems where non-strictly competitive agents feature common and conflicting interests. In this setting, conflicts arise when self-interested agents put their plans together and the resulting combination renders some of the plans non-executable, which implies a utility loss for the affected agents. Each participant wishes to execute its plan as it was conceived, but congestion issues and conflicts among the actions of the different plans compel agents to find a coordinated stable solution. Non-cooperative MAP tasks are tackled through non-cooperative games, which aim at finding a stable (equilibrium) joint plan that ensures the agents' plans are executable (by addressing planning conflicts) while accounting for their private interests as much as possible. Although this paradigm reflects many real-life problems, there is a lack of computational approaches to non-cooperative MAP in the literature. This PhD thesis pursues the application of non-cooperative games to solve non-cooperative MAP tasks that feature rational self-interested agents. Each agent calculates a plan that attains its individual planning task, and subsequently, the participants try to execute their plans in a shared environment. We tackle non-cooperative MAP from a twofold perspective. On the one hand, we focus on agents' satisfaction by studying desirable properties of stable solutions, such as optimality and fairness. On the other hand, we look for a combination of MAP and game-theoretic techniques capable of efficiently computing stable joint plans while minimizing the computational complexity of this combined task. Additionally, we consider planning conflicts and congestion issues in the agents' utility functions, which results in a more realistic approach. To the best of our knowledge, this PhD thesis opens up a new research line in non-cooperative MAP and establishes the basic principles to attain the problem of synthesizing stable joint plans for self-interested planning agents through the combination of game theory and automated planning.La Planificación Multi-Agente (PMA) es un tema de creciente interés que trata el problema de la planificación automática en dominios donde múltiples agentes planifican y actúan en un entorno compartido. En la mayoría de casos, los agentes en PMA son cooperativos (altruistas) y trabajan juntos para obtener una solución colaborativa. Sin embargo, cuando los agentes involucrados en una tarea de PMA son racionales y auto-interesados, el objetivo último es obtener un plan conjunto que resuelva las tareas locales de los agentes y satisfaga sus intereses privados. De entre los distintos escenarios de PMA que involucran agentes auto-interesados, la PMA no cooperativa se centra en problemas que presentan un conjunto de agentes no estrictamente competitivos con intereses comunes y conflictivos. En este contexto, pueden surgir conflictos cuando los agentes ponen en común sus planes y la combinación resultante provoca que algunos de estos planes no sean ejecutables, lo que implica una pérdida de utilidad para los agentes afectados. Cada participante desea ejecutar su plan tal como fue concebido, pero las congestiones y conflictos que pueden surgir entre las acciones de los diferentes planes fuerzan a los agentes a obtener una solución estable y coordinada. Las tareas de PMA no cooperativa se abordan a través de juegos no cooperativos, cuyo objetivo es hallar un plan conjunto estable (equilibrio) que asegure que los planes de los agentes sean ejecutables (resolviendo los conflictos de planificación) al tiempo que los agentes satisfacen sus intereses privados en la medida de lo posible. Aunque este paradigma refleja muchos problemas de la vida real, existen pocos enfoques computacionales para PMA no cooperativa en la literatura. Esta tesis doctoral estudia el uso de juegos no cooperativos para resolver tareas de PMA no cooperativa con agentes racionales auto-interesados. Cada agente calcula un plan para su tarea de planificación y posteriormente, los participantes intentan ejecutar sus planes en un entorno compartido. Abordamos la PMA no cooperativa desde una doble perspectiva. Por una parte, nos centramos en la satisfacción de los agentes estudiando las propiedades deseables de soluciones estables, tales como la optimalidad y la justicia. Por otra parte, buscamos una combinación de PMA y técnicas de teoría de juegos capaz de calcular planes conjuntos estables de forma eficiente al tiempo que se minimiza la complejidad computacional de esta tarea combinada. Además, consideramos los conflictos de planificación y congestiones en las funciones de utilidad de los agentes, lo que resulta en un enfoque más realista. Bajo nuestro punto de vista, esta tesis doctoral abre una nueva línea de investigación en PMA no cooperativa y establece los principios básicos para resolver el problema de la generación de planes conjuntos estables para agentes de planificación auto-interesados mediante la combinación de teoría de juegos y planificación automática.La Planificació Multi-Agent (PMA) és un tema de creixent interès que tracta el problema de la planificació automàtica en dominis on múltiples agents planifiquen i actuen en un entorn compartit. En la majoria de casos, els agents en PMA són cooperatius (altruistes) i treballen junts per obtenir una solució col·laborativa. No obstant això, quan els agents involucrats en una tasca de PMA són racionals i auto-interessats, l'objectiu últim és obtenir un pla conjunt que resolgui les tasques locals dels agents i satisfaci els seus interessos privats. D'entre els diferents escenaris de PMA que involucren agents auto-interessats, la PMA no cooperativa se centra en problemes que presenten un conjunt d'agents no estrictament competitius amb interessos comuns i conflictius. En aquest context, poden sorgir conflictes quan els agents posen en comú els seus plans i la combinació resultant provoca que alguns d'aquests plans no siguin executables, el que implica una pèrdua d'utilitat per als agents afectats. Cada participant vol executar el seu pla tal com va ser concebut, però les congestions i conflictes que poden sorgir entre les accions dels diferents plans forcen els agents a obtenir una solució estable i coordinada. Les tasques de PMA no cooperativa s'aborden a través de jocs no cooperatius, en els quals l'objectiu és trobar un pla conjunt estable (equilibri) que asseguri que els plans dels agents siguin executables (resolent els conflictes de planificació) alhora que els agents satisfan els seus interessos privats en la mesura del possible. Encara que aquest paradigma reflecteix molts problemes de la vida real, hi ha pocs enfocaments computacionals per PMA no cooperativa en la literatura. Aquesta tesi doctoral estudia l'ús de jocs no cooperatius per resoldre tasques de PMA no cooperativa amb agents racionals auto-interessats. Cada agent calcula un pla per a la seva tasca de planificació i posteriorment, els participants intenten executar els seus plans en un entorn compartit. Abordem la PMA no cooperativa des d'una doble perspectiva. D'una banda, ens centrem en la satisfacció dels agents estudiant les propietats desitjables de solucions estables, com ara la optimalitat i la justícia. D'altra banda, busquem una combinació de PMA i tècniques de teoria de jocs capaç de calcular plans conjunts estables de forma eficient alhora que es minimitza la complexitat computacional d'aquesta tasca combinada. A més, considerem els conflictes de planificació i congestions en les funcions d'utilitat dels agents, el que resulta en un enfocament més realista. Des del nostre punt de vista, aquesta tesi doctoral obre una nova línia d'investigació en PMA no cooperativa i estableix els principis bàsics per resoldre el problema de la generació de plans conjunts estables per a agents de planificació auto-interessats mitjançant la combinació de teoria de jocs i planificació automàtica.Jordán Prunera, JM. (2017). Non-Cooperative Games for Self-Interested Planning Agents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90417TESI

Teaching game theory and rationality to artificial intelligence master's students

[EN] Game theory is a branch of applied mathematics used in economics and artificial intelligence to study the behaviour of self-interested agents. In game theory, agents are rational, which means that they will always analyse the situation intelligently and seek their own benefit. The concept of rationality in game theory and artificial intelligence is based on the rigorous analysis of a conflict between self-interested agents in which they exclusively seek to maximise their utility in the defined terms of the game. Are we humans totally rational in making our decisions? Do we consider all the possible options to get the maximum benefit or do we let ourselves be influenced by other factors such as feelings? In this paper, we present the methods used with students of the Master's Degree in Artificial Intelligence at the Universitat Politècnica de València to teach game theory and rationality. In order to ensure that the students were able to assimilate the concepts of game theory, the classic master class method has been combined with new technologies to make the lesson dynamic. In this way, the explanation of theoretical concepts has been mixed with small games carried out with the students, always guided by the professor, using tools such as Kahoot and Socrative. The experience acquired during the lessons shows that even in the case of students coming from engineering backgrounds with a solid mathematical base, in general, they still lack this abstract understanding of rationality and act according to their feelings in many cases. This invites reflection and attempts to provide students with guidance for the correct understanding of the concept of rationality along with other complex concepts typical of game theory. On the other hand, we also show the results obtained from a series of games played in the classroom that demonstrate this lack of rationality in the students, what obviously makes them human. This can be interpreted as the fact that the utility functions of the agents that represent humans in a game do not capture all the variables, such as feelings, that a person takes into account when making certain types of decisions. The evaluation of the knowledge acquired by the students was done through an open project that consisted in designing a game including the rules of the game, applicable concepts of equilibrium, and analysis of the rationality of the players. This allowed the assessment of the knowledge acquired by the students during the lessons carried out with the dynamic methodology obtaining very satisfactory results. In this sense, the academic results of the students regarding the average grades obtained were higher than in previous courses in which this methodology was not applied. On the other hand, student satisfaction was also high, as shown by the surveys carried out.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government.Jordán, J.; Julian Inglada, VJ. (2021). Teaching game theory and rationality to artificial intelligence master's students. IATED. 3852-3858. https://doi.org/10.21125/inted.2021.0792S3852385

Using learning by doing methodology for teaching multi-agent systems

[EN] In recent years the teaching of subjects related to Artificial Intelligence has grown notably in higher education degrees. This is the case of the discipline of multi-agent systems, which usually is part of the majority of master's degrees in Artificial Intelligence. Multi-agent systems (MAS) offer solutions for distributed decision making, where a set of autonomous intelligent agents must reach an agreement to solve a problem. These types of problems are usually complex and distributed, difficult to abstract and simplify for classroom teaching. The main problem that teachers of this subject have to face, is to be able to integrate the whole set of related techniques and algorithms in a practical example that is easy to understand and address within the framework of the planning of a course. This paper deals with the use of the "learning by doing" methodology in a subject of multi-agent systems in the Master's Degree in Artificial Intelligence at the Universitat Politècnica de València. This methodology is applied by avoiding master classes to focus on practice. The classes become a scientific-technological experience. The students and the teacher are a team working with a common purpose, seeking to achieve a goal. To do this, the whole course has been reformulated, proposing the students to solve different typical problems of the MAS area on the same domain, in this case the improvement of urban mobility and the efficient use of energy in the cities. It is considered to be a sufficiently current topic that can motivate the student to participate and propose solutions. To achieve this objective, a multi-agent system tool has been developed that allows students to simulate the different situations proposed and develop solutions. The tool provides them with an urban simulation environment where they can easily introduce their own strategies to be carried out by each simulation agent. In this way, students are proposed different challenges where they can develop negotiation strategies to simulate the operation of urban taxi fleets, and cooperation strategies, where different agents help each other to achieve a common goal. This tool, called SimFleet, has been developed in an open way and published as open source, so that it can be used by any teaching team that wishes to do so, and even receive external contributions and improvements thanks to its open character. This learning by doing methodology supported by the SimFleet simulation tool has been applied in two consecutive academic years obtaining better results in student assessment and learning than in previous courses. Furthermore, the results of the student satisfaction surveys have shown a notable increase when using these technologies, which reinforces the idea that this type of learning is more useful and more satisfactory for students.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government.Palanca Cámara, J.; Jordán, J.; Julian Inglada, VJ. (2021). Using learning by doing methodology for teaching multi-agent systems. IATED. 3866-3871. https://doi.org/10.21125/inted.2021.0794S3866387

Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs

[EN] New challenges in education require new ways of education. Higher education has adapted to these new challenges by means of offering new types of training like massive online open courses and by updating their teaching methodology using novel approaches as flipped classrooms. These types of training have enabled universities to better adapt to the challenges posed by the pandemic. In addition, high quality learning objects are necessary for these new forms of education to be successful, with learning videos being the most common learning objects to provide theoretical concepts. This paper describes a new approach of a previously presented hybrid learning recommender system based on content-based techniques, which was capable of recommend useful videos to learners and lecturers from a learning video repository. In this new approach, the content-based techniques are also combined with a collaborative filtering module, which increases the probability of recommending relevant videos. This hybrid technique has been successfully applied to a real scenario in the central video repository of the Universitat Politècnica de València.This research was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti, V. (2021). Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs. Electronics. 10(11):1-19. https://doi.org/10.3390/electronics10111226S119101

The use of podcasting for a hybrid flipped classroom

[EN] We are currently assisting a social paradigm change motivated by the incorporation, more and more accelerated, of new information technologies (e.g., social networks or content platforms) in our day-today life. The increased availability of online resources in a universal, diverse, and permanent way is modifying how we consume online information and content. This is especially true when it comes to children and teenagers. New generations are increasingly adapting to immediacy and communication through information technologies. Therefore, it is necessary to evolve the educational paradigm to adapt it to this new social reality. Future learning strategies should consider the latest models of social communication, adapting them to achieve learning objectives from the perspective of constructive alignment and the acquisition or improvement of transversal competencies. In this sense, there are currently several technologies that can be incorporated into the classroom. One of the emerging technologies is the podcast. Usually used for entertainment (e.g., stories, books, or radio talks), the podcast is becoming a tool for massive online content distribution. There exist many advantages to using podcasting for an educative purpose, as the production cost (in terms of time) is lower than recording a video. From a content consumer perspective, the main advantage is that an audio-only approach can be used/consumed everywhere and more easily than video media. Some educational podcasts are available online but generally tend to focus on learning languages or history. Outside these specific topics, their use in technology subjects is still residual and mainly focuses on interviews or long expositions. Moreover, in current proposals, the teacher is the one who produces the podcast, and therefore it is a one-way communication model (from the teacher to the students). On the other hand, one of the teaching innovation models that is being increasingly used is the flipped classroom. In the flipped classroom, students are the main protagonists in their learning. They must prepare the theoretical parts on their own, and the teacher serves as a guide during this learning process. In this paper, we propose using the podcast in the flipped classroom model, turning the podcast into a bidirectional tool in which students are both producers and receivers of learning. The proposal consists of dividing the class into small groups of students (2-4). Each group will record a podcast episode on a different conceptual part of the lesson. Group members will have to coordinate and share the activities of recording and searching for content for the podcast. This will encourage transversal skills related to communication and social skills. These podcasts will then be shared with the rest of the groups so that everyone can have direct and permanent access to the different sections of the lesson. Creativity will also be encouraged, allowing students to add the music or sound effects they consider necessary to enhance the explanation.The authors gratefully acknowledge the financial support of Consellería d'Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana and the European Social Fund (Investing In Your Future) (APOSTD/2021/227 and CIPROM/2021/077), the Spanish Ministry of Science (project PID2021-123673OB-C31) and the Research Services of Universitat Politècnica de València. Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR".Marco-Detchart, C.; Taverner-Aparicio, JJ.; Jordán, J. (2023). The use of podcasting for a hybrid flipped classroom. IATED. 3119-3124. https://doi.org/10.21125/inted.2023.08633119312

An Abstract Framework for Non-Cooperative Multi-Agent Planning

[EN] In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents¿ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.This work was partially funded by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by Universitat Politecnica de Valencia (UPV) PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana Fondo Social Europeo.Jordán, J.; Bajo, J.; Botti, V.; Julian Inglada, VJ. (2019). An Abstract Framework for Non-Cooperative Multi-Agent Planning. Applied Sciences. 9(23):1-18. https://doi.org/10.3390/app9235180S118923De Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. Multiagent and Grid Systems, 5(4), 345-355. doi:10.3233/mgs-2009-0133Dunne, P. E., Kraus, S., Manisterski, E., & Wooldridge, M. (2010). Solving coalitional resource games. Artificial Intelligence, 174(1), 20-50. doi:10.1016/j.artint.2009.09.005Torreño, A., Onaindia, E., Komenda, A., & Štolba, M. (2018). Cooperative Multi-Agent Planning. ACM Computing Surveys, 50(6), 1-32. doi:10.1145/3128584Fikes, R. E., & Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4), 189-208. doi:10.1016/0004-3702(71)90010-5Hoffmann, J., & Nebel, B. (2001). The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, 253-302. doi:10.1613/jair.855Dukeman, A., & Adams, J. A. (2017). Hybrid mission planning with coalition formation. Autonomous Agents and Multi-Agent Systems, 31(6), 1424-1466. doi:10.1007/s10458-017-9367-7Hadad, M., Kraus, S., Ben-Arroyo Hartman, I., & Rosenfeld, A. (2013). Group planning with time constraints. Annals of Mathematics and Artificial Intelligence, 69(3), 243-291. doi:10.1007/s10472-013-9363-9Guo, Y., Pan, Q., Sun, Q., Zhao, C., Wang, D., & Feng, M. (2019). Cooperative Game-based Multi-Agent Path Planning with Obstacle Avoidance*. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). doi:10.1109/isie.2019.8781205v. Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(1), 295-320. doi:10.1007/bf01448847Mookherjee, D., & Sopher, B. (1994). Learning Behavior in an Experimental Matching Pennies Game. Games and Economic Behavior, 7(1), 62-91. doi:10.1006/game.1994.1037Ochs, J. (1995). Games with Unique, Mixed Strategy Equilibria: An Experimental Study. Games and Economic Behavior, 10(1), 202-217. doi:10.1006/game.1995.1030Applegate, C., Elsaesser, C., & Sanborn, J. (1990). An architecture for adversarial planning. IEEE Transactions on Systems, Man, and Cybernetics, 20(1), 186-194. doi:10.1109/21.47820Sailer, F., Buro, M., & Lanctot, M. (2007). Adversarial Planning Through Strategy Simulation. 2007 IEEE Symposium on Computational Intelligence and Games. doi:10.1109/cig.2007.368082Willmott, S., Richardson, J., Bundy, A., & Levine, J. (2001). Applying adversarial planning techniques to Go. Theoretical Computer Science, 252(1-2), 45-82. doi:10.1016/s0304-3975(00)00076-1Nau, D. S., Au, T. C., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., & Yaman, F. (2003). SHOP2: An HTN Planning System. Journal of Artificial Intelligence Research, 20, 379-404. doi:10.1613/jair.1141Knuth, D. E., & Moore, R. W. (1975). An analysis of alpha-beta pruning. Artificial Intelligence, 6(4), 293-326. doi:10.1016/0004-3702(75)90019-3Vickrey, W. (1961). COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS. The Journal of Finance, 16(1), 8-37. doi:10.1111/j.1540-6261.1961.tb02789.xClarke, E. H. (1971). Multipart pricing of public goods. Public Choice, 11(1), 17-33. doi:10.1007/bf01726210Groves, T. (1973). Incentives in Teams. Econometrica, 41(4), 617. doi:10.2307/1914085Savaux, J., Vion, J., Piechowiak, S., Mandiau, R., Matsui, T., Hirayama, K., … Silaghi, M. (2016). DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). doi:10.1109/wi.2016.0057Buzing, P., Mors, A. ter, Valk, J., & Witteveen, C. (2006). Coordinating Self-interested Planning Agents. Autonomous Agents and Multi-Agent Systems, 12(2), 199-218. doi:10.1007/s10458-005-6104-4Ter Mors, A., & Witteveen, C. (s. f.). Coordinating Non Cooperative Planning Agents: Complexity Results. IEEE/WIC/ACM International Conference on Intelligent Agent Technology. doi:10.1109/iat.2005.60Hrnčíř, J., Rovatsos, M., & Jakob, M. (2015). Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach. Journal of Intelligent Transportation Systems, 19(1), 89-105. doi:10.1080/15472450.2014.941759Galuszka, A., & Swierniak, A. (2009). Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy. Journal of Intelligent and Robotic Systems, 58(3-4), 239-251. doi:10.1007/s10846-009-9364-4Rosenthal, R. W. (1973). A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory, 2(1), 65-67. doi:10.1007/bf01737559Jordán, J., Torreño, A., de Weerdt, M., & Onaindia, E. (2017). A better-response strategy for self-interested planning agents. Applied Intelligence, 48(4), 1020-1040. doi:10.1007/s10489-017-1046-5Veloso, M., Muñoz-Avila, H., & Bergmann, R. (1996). Case-based planning: selected methods and systems. AI Communications, 9(3), 128-137. doi:10.3233/aic-1996-9305VOORNEVELD, M., BORM, P., VAN MEGEN, F., TIJS, S., & FACCHINI, G. (1999). CONGESTION GAMES AND POTENTIALS RECONSIDERED. International Game Theory Review, 01(03n04), 283-299. doi:10.1142/s0219198999000219Han-Lim Choi, Brunet, L., & How, J. P. (2009). Consensus-Based Decentralized Auctions for Robust Task Allocation. IEEE Transactions on Robotics, 25(4), 912-926. doi:10.1109/tro.2009.2022423Monderer, D., & Shapley, L. S. (1996). Potential Games. Games and Economic Behavior, 14(1), 124-143. doi:10.1006/game.1996.0044Friedman, J. W., & Mezzetti, C. (2001). Learning in Games by Random Sampling. Journal of Economic Theory, 98(1), 55-84. doi:10.1006/jeth.2000.2694Aamodt, A., & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1), 39-59. doi:10.3233/aic-1994-7104Bertsekas, D. P. (1988). The auction algorithm: A distributed relaxation method for the assignment problem. Annals of Operations Research, 14(1), 105-123. doi:10.1007/bf02186476Bertsekas, D. P., & Castanon, D. A. (1989). The auction algorithm for the transportation problem. Annals of Operations Research, 20(1), 67-96. doi:10.1007/bf0221692

An energy-aware algorithm for electric vehicle infrastructures in smart cities

[EN] The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating a set of charging stations in a smart city considering heterogeneous data sources such as open data city portals, geo-located social network data, and energy transformer substations. We use a multi-objective genetic algorithm to optimize the charging station locations by maximizing the utility and minimizing the cost. Our proposal is validated through a case study and several experimental results.This work was partially supported by MINECO/FEDER, Spain RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by UPV, Spain PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana -Fondo Social Europeo, Spain.Palanca Cámara, J.; Jordán, J.; Bajo, J.; Botti Navarro, VJ. (2020). An energy-aware algorithm for electric vehicle infrastructures in smart cities. Future Generation Computer Systems. 108:454-466. https://doi.org/10.1016/j.future.2020.03.001S454466108Gan, L., Topcu, U., & Low, S. H. (2013). Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems, 28(2), 940-951. doi:10.1109/tpwrs.2012.2210288Ma, T., & Mohammed, O. A. (2014). Optimal Charging of Plug-in Electric Vehicles for a Car-Park Infrastructure. IEEE Transactions on Industry Applications, 50(4), 2323-2330. doi:10.1109/tia.2013.2296620Needell, Z. A., McNerney, J., Chang, M. T., & Trancik, J. E. (2016). Potential for widespread electrification of personal vehicle travel in the United States. Nature Energy, 1(9). doi:10.1038/nenergy.2016.112Franke, T., & Krems, J. F. (2013). Understanding charging behaviour of electric vehicle users. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 75-89. doi:10.1016/j.trf.2013.09.002Shukla, A., Pekny, J., & Venkatasubramanian, V. (2011). An optimization framework for cost effective design of refueling station infrastructure for alternative fuel vehicles. Computers & Chemical Engineering, 35(8), 1431-1438. doi:10.1016/j.compchemeng.2011.03.018Nie, Y. (Marco), & Ghamami, M. (2013). A corridor-centric approach to planning electric vehicle charging infrastructure. Transportation Research Part B: Methodological, 57, 172-190. doi:10.1016/j.trb.2013.08.010Tu, W., Li, Q., Fang, Z., Shaw, S., Zhou, B., & Chang, X. (2016). Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach. Transportation Research Part C: Emerging Technologies, 65, 172-189. doi:10.1016/j.trc.2015.10.004Dong, J., Liu, C., & Lin, Z. (2014). Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. Transportation Research Part C: Emerging Technologies, 38, 44-55. doi:10.1016/j.trc.2013.11.001He, J., Yang, H., Tang, T.-Q., & Huang, H.-J. (2018). An optimal charging station location model with the consideration of electric vehicle’s driving range. Transportation Research Part C: Emerging Technologies, 86, 641-654. doi:10.1016/j.trc.2017.11.026Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding Human Mobility from Twitter. PLOS ONE, 10(7), e0131469. doi:10.1371/journal.pone.0131469Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-

Evaluating the use of self-video teaching in a flipped classroom

[EN] New generations are increasingly becoming more familiar with consuming audio-visual material through online platforms. Consequently, learning through IT-based tools is becoming more and more common. Nowadays, learning platforms (e.g., edX or W3Schools) or content platforms (e.g., YouTube) containing vast amounts of courses and video tutorials are becoming increasingly popular among students. The main advantage of online learning is that students can access the content from anywhere and whenever they want, being able to revisit the content to review concepts and improve their level of knowledge. In this way, learning based on a deep approach and self-learning is promoted since students are the ones who regulate their learning process by deciding how much time to dedicate and when to do it. Appropriately using this type of resource can become a very effective tool applied to a flipped classroom model. In the flipped classroom model, students are active learners since they are in charge of developing the lesson material both in class and at home. In this type of learning, the teacher assumes the role of guide assisting during the learning process. A standard methodology in this flipped classroom model consists of students preparing different parts of the course content and then explaining those parts to their classmates. In this way, students develop a sense of responsibility toward the rest of their classmates, creating an environment where they can recognise their shortcomings and take control of their learning to teach others. In addition, the acquisition of transversal communication skills is encouraged. With all this in mind, in this article, we describe a case study we are currently carrying out with students enrolled in the programming course at the Universitat Politècnica de València. Our proposal combines the flipped classroom model with access to online resources. In this first approach, we have proposed that the students record a video explaining a part of the lesson or how to solve at least two exercises step by step. The explanation must be done as if they were content creators, and their audience were beginner programmers. The students will upload the videos to a private YouTube channel that will only be accessible to their classmates. In the classroom, the teacher will encourage students to share their stories and experiences while learning, editing, and recording the videos. This proposal's main objective is to promote students' engagement in the learning process and offer them learning alternatives through online content with a closer language that they can access whenever they need it. To motivate participation, students and teachers will choose the three best videos from all the videos. The three winners will receive extra points in the evaluation of the course.The authors gratefully acknowledge the financial support of Consellería d'Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana and the European Social Fund (Investing In Your Future) (APOSTD/2021/227 and CIPROM/2021/077), the Spanish Ministry of Science (project PID2021-123673OB-C31) and the Research Services of Universitat Politècnica de València. Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR".Taverner-Aparicio, JJ.; Marco-Detchart, C.; Jordán, J.; Vos, TE. (2023). Evaluating the use of self-video teaching in a flipped classroom. IATED. 3133-3138. https://doi.org/10.21125/inted.2023.08653133313

Demand-Responsive Shared Transportation: A Self-Interested Proposal

[EN] With the world population highly increasing, efficient methods of transportation are more necessary than ever. On the other hand, the sharing economy must be explored and applied where possible, aiming to palliate the effects of human development on the environment. In this paper we explore demand-responsive shared transportation as a system with the potential to serve its users' displacement needs while being less polluting. In contrast with previous works, we focus on a distributed proposal that allows each vehicle to retain its private information. Our work describes a partially dynamic system in which the vehicles are self-interested: they decide which users to serve according to the benefit it reports them. With our modelling, the system can be adapted to mobility platforms of autonomous drivers and even simulate the competition among different companies.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 Marti is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana".Martí, P.; Jordán, J.; De La Prieta, F.; Billhardt, H.; Julian, V. (2022). Demand-Responsive Shared Transportation: A Self-Interested Proposal. Electronics. 11(1):1-14. https://doi.org/10.3390/electronics1101007811411