48 research outputs found

    Recommending Learning Objects with Arguments and Explanations

    Full text link
    [EN] The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system's operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government, and by the Generalitat Valenciana (PROMETEO/2018/002) project.Heras, S.; Palanca Cámara, J.; Rodriguez, P.; Duque-Méndez, N.; Julian Inglada, VJ. (2020). Recommending Learning Objects with Arguments and Explanations. Applied Sciences. 10(10):1-18. https://doi.org/10.3390/app10103341S1181010Zapalska, A., & Brozik, D. (2006). Learning styles and online education. Campus-Wide Information Systems, 23(5), 325-335. doi:10.1108/10650740610714080Rodríguez, P., Heras, S., Palanca, J., Poveda, J. M., Duque, N., & Julián, V. (2017). An educational recommender system based on argumentation theory. AI Communications, 30(1), 19-36. doi:10.3233/aic-170724Chen, L., & Pu, P. (2011). Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1-2), 125-150. doi:10.1007/s11257-011-9108-6He, C., Parra, D., & Verbert, K. (2016). Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56, 9-27. doi:10.1016/j.eswa.2016.02.013Vig, J., Sen, S., & Riedl, J. (2009). Tagsplanations. Proceedings of the 14th international conference on Intelligent user interfaces. doi:10.1145/1502650.1502661Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2009). MoviExplain. Proceedings of the third ACM conference on Recommender systems - RecSys ’09. doi:10.1145/1639714.1639777Fogg, B. J. (2002). Persuasive technology. Ubiquity, 2002(December), 2. doi:10.1145/764008.763957Benbasat, I., & Wang, W. (2005). Trust In and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101. doi:10.17705/1jais.00065Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27-30. doi:10.5120/7218-0024Salehi, M., Pourzaferani, M., & Razavi, S. A. (2013). Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egyptian Informatics Journal, 14(1), 67-78. doi:10.1016/j.eij.2012.12.001Dwivedi, P., & Bharadwaj, K. K. (2013). e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Systems, 32(2), 264-276. doi:10.1111/exsy.12061Tarus, J. K., Niu, Z., & Mustafa, G. (2017). Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21-48. doi:10.1007/s10462-017-9539-5BRIGUEZ, C. E., CAPOBIANCO, M., & MAGUITMAN, A. G. (2013). A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION. International Journal on Artificial Intelligence Tools, 22(04), 1350021. doi:10.1142/s0218213013500218Recio-García, J. A., Quijano, L., & Díaz-Agudo, B. (2013). Including social factors in an argumentative model for Group Decision Support Systems. Decision Support Systems, 56, 48-55. doi:10.1016/j.dss.2013.05.007Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014). Argument-based mixed recommenders and their application to movie suggestion. Expert Systems with Applications, 41(14), 6467-6482. doi:10.1016/j.eswa.2014.03.046Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571-604. doi:10.1007/s10462-015-9440-zThe VARK Questionnaire-Spanish Versionhttps://vark-learn.com/wp-content/uploads/2014/08/The-VARK-Questionnaire-Spanish.pdfGARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s1471068403001674Gelfond, M., & Lifschitz, V. (1991). Classical negation in logic programs and disjunctive databases. New Generation Computing, 9(3-4), 365-385. doi:10.1007/bf03037169Snow, R. E. (1991). Aptitude-treatment interaction as a framework for research on individual differences in psychotherapy. Journal of Consulting and Clinical Psychology, 59(2), 205-216. doi:10.1037/0022-006x.59.2.20

    On the Extraction and Use of Arguments in Recommender Systems: A Case Study in the E-participation Domain

    Full text link
    In this paper, we present ongoing work on the automatic extraction of arguments from textual content, and on the use of interconnected argument structures by recommender systems. Differently to the majority of existing argument mining methods –which only consider ‘premise’ and ‘claim’ as the components of an argument, and ‘support’ and ‘attack’ as the possible relations between argument components–, we propose an argumentation model based on a detailed taxonomy of argumentative relations. Moreover, we provide a lexicon of English and Spanish linguistic connectors categorized in our taxonomy. As a proof of concept, we apply a simple, yet effective method that makes use of the built taxonomy and lexicon to extract argument graphs from citizen proposals and debates of an e-participation platform. We then describe how the extracted graphs could be exploited to generate and explain argument-based recommendationsThis work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00

    Defeasible-argumentation-based multi-agent planning

    Full text link
    [EN] This paper presents a planning system that uses defeasible argumentation to reason about context information during the construction of a plan. The system is designed to operate in cooperative multi-agent environments where agents are endowed with planning and argumentation capabilities. Planning allows agents to contribute with actions to the construction of the plan, and argumentation is the mechanism that agents use to defend or attack the planning choices according to their beliefs. We present the formalization of the model and we provide a novel specification of the qualification problem. The multi-agent planning system, which is designed to be domain-independent, is evaluated with two planning tasks from the problem suites of the International Planning Competition. We compare our system with a non-argumentative planning framework and with a different approach of planning and argumentation. The results will show that our system obtains less costly and more robust solution plans.This work has been partly supported by the Spanish MINECO under project TIN2014-55637-C2-2-R and the Valencian project PROMETEO II/2013/019.Pajares Ferrando, S.; Onaindia De La Rivaherrera, E. (2017). Defeasible-argumentation-based multi-agent planning. Information Sciences. 411:1-22. https://doi.org/10.1016/j.ins.2017.05.014S12241

    Defeasible Argumentation for Cooperative Multi-Agent Planning

    Full text link
    Tesis por compendio[EN] Multi-Agent Systems (MAS), Argumentation and Automated Planning are three lines of investigations within the field of Artificial Intelligence (AI) that have been extensively studied over the last years. A MAS is a system composed of multiple intelligent agents that interact with each other and it is used to solve problems whose solution requires the presence of various functional and autonomous entities. Multi-agent systems can be used to solve problems that are difficult or impossible to resolve for an individual agent. On the other hand, Argumentation refers to the construction and subsequent exchange (iteratively) of arguments between a group of agents, with the aim of arguing for or against a particular proposal. Regarding Automated Planning, given an initial state of the world, a goal to achieve, and a set of possible actions, the goal is to build programs that can automatically calculate a plan to reach the final state from the initial state. The main objective of this thesis is to propose a model that combines and integrates these three research lines. More specifically, we consider a MAS as a team of agents with planning and argumentation capabilities. In that sense, given a planning problem with a set of objectives, (cooperative) agents jointly construct a plan to satisfy the objectives of the problem while they defeasibly reason about the environmental conditions so as to provide a stronger guarantee of success of the plan at execution time. Therefore, the goal is to use the planning knowledge to build a plan while agents beliefs about the impact of unexpected environmental conditions is used to select the plan which is less likely to fail at execution time. Thus, the system is intended to return collaborative plans that are more robust and adapted to the circumstances of the execution environment. In this thesis, we designed, built and evaluated a model of argumentation based on defeasible reasoning for planning cooperative multi-agent system. The designed system is independent of the domain, thus demonstrating the ability to solve problems in different application contexts. Specifically, the system has been tested in context sensitive domains such as Ambient Intelligence as well as with problems used in the International Planning Competitions.[ES] Dentro de la Inteligencia Artificial (IA), existen tres ramas que han sido ampliamente estudiadas en los últimos años: Sistemas Multi-Agente (SMA), Argumentación y Planificación Automática. Un SMA es un sistema compuesto por múltiples agentes inteligentes que interactúan entre sí y se utilizan para resolver problemas cuya solución requiere la presencia de diversas entidades funcionales y autónomas. Los sistemas multiagente pueden ser utilizados para resolver problemas que son difíciles o imposibles de resolver para un agente individual. Por otra parte, la Argumentación consiste en la construcción y posterior intercambio (iterativamente) de argumentos entre un conjunto de agentes, con el objetivo de razonar a favor o en contra de una determinada propuesta. Con respecto a la Planificación Automática, dado un estado inicial del mundo, un objetivo a alcanzar, y un conjunto de acciones posibles, el objetivo es construir programas capaces de calcular de forma automática un plan que permita alcanzar el estado final a partir del estado inicial. El principal objetivo de esta tesis es proponer un modelo que combine e integre las tres líneas anteriores. Más específicamente, nosotros consideramos un SMA como un equipo de agentes con capacidades de planificación y argumentación. En ese sentido, dado un problema de planificación con un conjunto de objetivos, los agentes (cooperativos) construyen conjuntamente un plan para resolver los objetivos del problema y, al mismo tiempo, razonan sobre la viabilidad de los planes, utilizando como herramienta de diálogo la Argumentación. Por tanto, el objetivo no es sólo obtener automáticamente un plan solución generado de forma colaborativa entre los agentes, sino también utilizar las creencias de los agentes sobre la información del contexto para razonar acerca de la viabilidad de los planes en su futura etapa de ejecución. De esta forma, se pretende que el sistema sea capaz de devolver planes colaborativos más robustos y adaptados a las circunstancias del entorno de ejecución. En esta tesis se diseña, construye y evalúa un modelo de argumentación basado en razonamiento defeasible para un sistema de planificación cooperativa multiagente. El sistema diseñado es independiente del dominio, demostrando así la capacidad de resolver problemas en diferentes contextos de aplicación. Concretamente el sistema se ha evaluado en dominios sensibles al contexto como es la Inteligencia Ambiental y en problemas de las competiciones internacionales de planificación.[CA] Dins de la intel·ligència artificial (IA), hi han tres branques que han sigut àmpliament estudiades en els últims anys: Sistemes Multi-Agent (SMA), Argumentació i Planificació Automàtica. Un SMA es un sistema compost per múltiples agents intel·ligents que interactúen entre si i s'utilitzen per a resoldre problemas la solución dels quals requereix la presència de diverses entitats funcionals i autònomes. Els sistemes multiagente poden ser utilitzats per a resoldre problemes que són difícils o impossibles de resoldre per a un agent individual. D'altra banda, l'Argumentació consistiex en la construcció i posterior intercanvi (iterativament) d'arguments entre un conjunt d'agents, amb l'objectiu de raonar a favor o en contra d'una determinada proposta. Respecte a la Planificació Automàtica, donat un estat inicial del món, un objectiu a aconseguir, i un conjunt d'accions possibles, l'objectiu és construir programes capaços de calcular de forma automàtica un pla que permeta aconseguir l'estat final a partir de l'estat inicial. El principal objectiu d'aquesta tesi és proposar un model que combine i integre les tres línies anteriors. Més específicament, nosaltres considerem un SMA com un equip d'agents amb capacitats de planificació i argumentació. En aquest sentit, donat un problema de planificació amb un conjunt d'objectius, els agents (cooperatius) construeixen conjuntament un pla per a resoldre els objectius del problema i, al mateix temps, raonen sobre la viabilitat dels plans, utilitzant com a ferramenta de diàleg l'Argumentació. Per tant, l'objectiu no és només obtindre automàticament un pla solució generat de forma col·laborativa entre els agents, sinó també utilitzar les creences dels agents sobre la informació del context per a raonar sobre la viabilitat dels plans en la seua futura etapa d'execució. D'aquesta manera, es pretén que el sistema siga capaç de tornar plans col·laboratius més robustos i adaptats a les circumstàncies de l'entorn d'execució. En aquesta tesi es dissenya, construeix i avalua un model d'argumentació basat en raonament defeasible per a un sistema de planificació cooperativa multiagent. El sistema dissenyat és independent del domini, demostrant així la capacitat de resoldre problemes en diferents contextos d'aplicació. Concretament el sistema s'ha avaluat en dominis sensibles al context com és la inte·ligència Ambiental i en problemes de les competicions internacionals de planificació.Pajares Ferrando, S. (2016). Defeasible Argumentation for Cooperative Multi-Agent Planning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/60159TESISCompendi

    An educational recommender system based on argumentation theory

    Full text link
    You are free to use the manuscript version of your article for internal, educational or other purposes of your own institution, company or funding agency[EN] Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student's characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybridization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a new recommendation method based on argumentation theory that is able to combine content-based, collaborative and knowledge-based recommendation techniques, or to act as a new recommendation technique. This method provides the students with those objects for which the system is able to generate more arguments to justify their suitability. It has been implemented and tested in the Federation of Learning Objects Repositories of Colombia, getting promising results.This work was partially developed with the aid of the doctoral grant offered to Paula A. Rodriguez by 'Programa Nacional de Formacion de Investigadores - COLCIENCIAS', Colombia and partially funded by the COLCIENCIAS project 1119-569-34172 from the Universidad Nacional de Colombia. It was also supported by the by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politecnica de Valencia.Rodríguez, P.; Heras, S.; Palanca Cámara, J.; Poveda, JM.; Duque, N.; Julian Inglada, VJ. (2017). An educational recommender system based on argumentation theory. AI Communications. 30(1):19-36. https://doi.org/10.3233/AIC-170724S1936301Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014). Argument-based mixed recommenders and their application to movie suggestion. Expert Systems with Applications, 41(14), 6467-6482. doi:10.1016/j.eswa.2014.03.046BRIGUEZ, C. E., CAPOBIANCO, M., & MAGUITMAN, A. G. (2013). A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION. International Journal on Artificial Intelligence Tools, 22(04), 1350021. doi:10.1142/s0218213013500218R. Burke, Hybrid recommender systems: Survey and experiments, User Modelingand User-Adapted Interaction (2002).Chesñevar, C., Maguitman, A. G., & González, M. P. (2009). Empowering Recommendation Technologies Through Argumentation. Argumentation in Artificial Intelligence, 403-422. doi:10.1007/978-0-387-98197-0_20Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. Recommender Systems Handbook, 421-451. doi:10.1007/978-1-4899-7637-6_12N.D. Duque, D.A. Ovalle and J. Moreno, Objetos de aprendizaje, repositorios y federaciones... conocimiento para todos. Universidad Nacional de Colombia, 2015.Dwivedi, P., & Bharadwaj, K. K. (2013). e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Systems, 32(2), 264-276. doi:10.1111/exsy.12061GARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s1471068403001674Gunawardana, A., & Shani, G. (2015). Evaluating Recommender Systems. Recommender Systems Handbook, 265-308. doi:10.1007/978-1-4899-7637-6_8Heras, S., Botti, V., & Julián, V. (2012). Argument-based agreements in agent societies. Neurocomputing, 75(1), 156-162. doi:10.1016/j.neucom.2011.02.022Heras, S., Rebollo, M., & Julián, V. (s. f.). A Dialogue Game Protocol for Recommendation in Social Networks. Hybrid Artificial Intelligence Systems, 515-522. doi:10.1007/978-3-540-87656-4_64P.A. Kirschner, S.J. Buckingham-Shum and C.S. Carr, Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making, Springer Science & Business Media, 2012.Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571-604. doi:10.1007/s10462-015-9440-zLearning Technology Standards Committee, IEEE Standard for Learning Object Metadata, Institute of Electrical and Electronics Engineers, New York, 2002.Leite, W. L., Svinicki, M., & Shi, Y. (2009). Attempted Validation of the Scores of the VARK: Learning Styles Inventory With Multitrait–Multimethod Confirmatory Factor Analysis Models. Educational and Psychological Measurement, 70(2), 323-339. doi:10.1177/0013164409344507Li, H., Oren, N., & Norman, T. J. (2012). Probabilistic Argumentation Frameworks. Lecture Notes in Computer Science, 1-16. doi:10.1007/978-3-642-29184-5_1CACM Staff. (2009). Recommendation algorithms, online privacy, and more. Communications of the ACM, 52(5), 10-11. doi:10.1145/1506409.1506434Ossowski, S., Sierra, C., & Botti, V. (2012). Agreement Technologies: A Computing Perspective. Agreement Technologies, 3-16. doi:10.1007/978-94-007-5583-3_1Palanca, J., Heras, S., Jorge, J., & Julian, V. (2015). Towards persuasive social recommendation. ACM SIGAPP Applied Computing Review, 15(2), 41-49. doi:10.1145/2815169.2815173Recio-García, J. A., Quijano, L., & Díaz-Agudo, B. (2013). Including social factors in an argumentative model for Group Decision Support Systems. Decision Support Systems, 56, 48-55. doi:10.1016/j.dss.2013.05.007Rodríguez, P., Duque, N., & Ovalle, D. A. (2015). Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering. Communications in Computer and Information Science, 356-364. doi:10.1007/978-3-319-19033-4_31Rodríguez, P. A., Ovalle, D. A., & Duque, N. D. (2015). A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories. Learning and Collaboration Technologies, 291-300. doi:10.1007/978-3-319-20609-7_28M. Salehi, M. Pourzaferani and S.A. Razavi, Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model, Egyptian Informatics Journal (2013).Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27-30. doi:10.5120/7218-0024Sinha, R., & Swearingen, K. (2002). The role of transparency in recommender systems. CHI ’02 extended abstracts on Human factors in computing systems - CHI ’02. doi:10.1145/506443.506619Van de Sompel, H., Chute, R., & Hochstenbach, P. (2008). The aDORe federation architecture: digital repositories at scale. International Journal on Digital Libraries, 9(2), 83-100. doi:10.1007/s00799-008-0048-7Vekariya, V., & Kulkarni, G. R. (2012). Notice of Violation of IEEE Publication Principles - Hybrid recommender systems: Survey and experiments. 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP). doi:10.1109/dictap.2012.621540

    Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

    Full text link
    As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders

    Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

    Full text link
    Tesis por compendio[ES] La argumentación computacional es el área de investigación que estudia y analiza el uso de distintas técnicas y algoritmos que aproximan el razonamiento argumentativo humano desde un punto de vista computacional. En esta tesis doctoral se estudia el uso de distintas técnicas propuestas bajo el marco de la argumentación computacional para realizar un análisis automático del discurso argumentativo, y para desarrollar técnicas de persuasión computacional basadas en argumentos. Con estos objetivos, en primer lugar se presenta una completa revisión del estado del arte y se propone una clasificación de los trabajos existentes en el área de la argumentación computacional. Esta revisión nos permite contextualizar y entender la investigación previa de forma más clara desde la perspectiva humana del razonamiento argumentativo, así como identificar las principales limitaciones y futuras tendencias de la investigación realizada en argumentación computacional. En segundo lugar, con el objetivo de solucionar algunas de estas limitaciones, se ha creado y descrito un nuevo conjunto de datos que permite abordar nuevos retos y investigar problemas previamente inabordables (e.g., evaluación automática de debates orales). Conjuntamente con estos datos, se propone un nuevo sistema para la extracción automática de argumentos y se realiza el análisis comparativo de distintas técnicas para esta misma tarea. Además, se propone un nuevo algoritmo para la evaluación automática de debates argumentativos y se prueba con debates humanos reales. Finalmente, en tercer lugar se presentan una serie de estudios y propuestas para mejorar la capacidad persuasiva de sistemas de argumentación computacionales en la interacción con usuarios humanos. De esta forma, en esta tesis se presentan avances en cada una de las partes principales del proceso de argumentación computacional (i.e., extracción automática de argumentos, representación del conocimiento y razonamiento basados en argumentos, e interacción humano-computador basada en argumentos), así como se proponen algunos de los cimientos esenciales para el análisis automático completo de discursos argumentativos en lenguaje natural.[CA] L'argumentació computacional és l'àrea de recerca que estudia i analitza l'ús de distintes tècniques i algoritmes que aproximen el raonament argumentatiu humà des d'un punt de vista computacional. En aquesta tesi doctoral s'estudia l'ús de distintes tècniques proposades sota el marc de l'argumentació computacional per a realitzar una anàlisi automàtic del discurs argumentatiu, i per a desenvolupar tècniques de persuasió computacional basades en arguments. Amb aquestos objectius, en primer lloc es presenta una completa revisió de l'estat de l'art i es proposa una classificació dels treballs existents en l'àrea de l'argumentació computacional. Aquesta revisió permet contextualitzar i entendre la investigació previa de forma més clara des de la perspectiva humana del raonament argumentatiu, així com identificar les principals limitacions i futures tendències de la investigació realitzada en argumentació computacional. En segon lloc, amb l'objectiu de sol\cdotlucionar algunes d'aquestes limitacions, hem creat i descrit un nou conjunt de dades que ens permet abordar nous reptes i investigar problemes prèviament inabordables (e.g., avaluació automàtica de debats orals). Conjuntament amb aquestes dades, es proposa un nou sistema per a l'extracció d'arguments i es realitza l'anàlisi comparativa de distintes tècniques per a aquesta mateixa tasca. A més a més, es proposa un nou algoritme per a l'avaluació automàtica de debats argumentatius i es prova amb debats humans reals. Finalment, en tercer lloc es presenten una sèrie d'estudis i propostes per a millorar la capacitat persuasiva de sistemes d'argumentació computacionals en la interacció amb usuaris humans. D'aquesta forma, en aquesta tesi es presenten avanços en cada una de les parts principals del procés d'argumentació computacional (i.e., l'extracció automàtica d'arguments, la representació del coneixement i raonament basats en arguments, i la interacció humà-computador basada en arguments), així com es proposen alguns dels fonaments essencials per a l'anàlisi automàtica completa de discursos argumentatius en llenguatge natural.[EN] Computational argumentation is the area of research that studies and analyses the use of different techniques and algorithms that approximate human argumentative reasoning from a computational viewpoint. In this doctoral thesis we study the use of different techniques proposed under the framework of computational argumentation to perform an automatic analysis of argumentative discourse, and to develop argument-based computational persuasion techniques. With these objectives in mind, we first present a complete review of the state of the art and propose a classification of existing works in the area of computational argumentation. This review allows us to contextualise and understand the previous research more clearly from the human perspective of argumentative reasoning, and to identify the main limitations and future trends of the research done in computational argumentation. Secondly, to overcome some of these limitations, we create and describe a new corpus that allows us to address new challenges and investigate on previously unexplored problems (e.g., automatic evaluation of spoken debates). In conjunction with this data, a new system for argument mining is proposed and a comparative analysis of different techniques for this same task is carried out. In addition, we propose a new algorithm for the automatic evaluation of argumentative debates and we evaluate it with real human debates. Thirdly, a series of studies and proposals are presented to improve the persuasiveness of computational argumentation systems in the interaction with human users. In this way, this thesis presents advances in each of the main parts of the computational argumentation process (i.e., argument mining, argument-based knowledge representation and reasoning, and argument-based human-computer interaction), and proposes some of the essential foundations for the complete automatic analysis of natural language argumentative discourses.This thesis has been partially supported by the Generalitat Valenciana project PROME- TEO/2018/002 and by the Spanish Government projects TIN2017-89156-R and PID2020- 113416RB-I00.Ruiz Dolz, R. (2023). Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/194806Compendi
    corecore