Reorganization in Dynamic Agent Societies

En la nueva era de tecnologías de la información, los sistemas tienden a ser cada vez más dinámicos, compuestos por entidades heterogéneas capaces de entrar y salir del sistema, interaccionar entre ellas, y adaptarse a las necesidades del entorno. Los sistemas multiagente han contribuído en los ultimos años, a modelar, diseñar e implementar sistemas autónomos con capacidad de interacción y comunicación. Estos sistemas se han modelado principalmente, a través de sociedades de agentes, las cuales facilitan la interación, organización y cooperación de agentes heterogéneos para conseguir diferentes objetivos. Para que estos paradigmas puedan ser utilizados para el desarrollo de nuevas generaciones de sistemas, características como dinamicidad y capacidad de reorganización deben estar incorporadas en el modelado, gestión y ejecución de estas sociedades de agentes. Concretamente, la reorganización en sociedades de agentes ofrece un paradigma para diseñar aplicaciones abiertas, dinámicas y adaptativas. Este proceso requiere determinar las consecuencias de cambiar el sistema, no sólo en términos de los beneficios conseguidos sinó además, midiendo los costes de adaptación así como el impacto que estos cambios tienen en todos los componentes del sistema. Las propuestas actuales de reorganización, básicamente abordan este proceso como respuestas de la sociedad cuando ocurre un cambio, o bien como un mecanismo para mejorar la utilidad del sistema. Sin embargo, no se pueden definir procesos complejos de decisión que obtengan la mejor configuración de los componentes organizacionales en cada momento, basándose en una evaluación de los beneficios que se podrían obtener así como de los costes asociados al proceso. Teniendo en cuenta este objetivo, esta tesis explora el área de reorganización en sociedades de agentes y se centra principalmente, en una propuesta novedosa para reorganización. Nuestra propuesta ofrece un soporte de toma de decisiones que considera cambios en múltiplesAlberola Oltra, JM. (2013). Reorganization in Dynamic Agent Societies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19243Palanci

Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets

The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and project TIN2011-27652-C03-01. Juan M. Alberola has received a grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289).Alberola Oltra, JM.; García Fornes, AM. (2013). Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets. Applied Intelligence. 38(3):465-477. https://doi.org/10.1007/s10489-012-0381-9S465477383Aamodt A (1990) Knowledge-intensive case-based reasoning and sustained learning. In: Topics in case-based reasoning. Springer, Berlin, pp 274–288Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Ahn JJ, Byun HW, Oh KJ, Kim TY (2012) Bayesian forecaster using class-based optimization. Appl Intell 36(3):553–563Alberola JM, Garcia-Fornes A, Espinosa A (2010) Price prediction in sports betting markets. In: Proceedings of the 8th German conference on multiagent system technologies, pp 197–208Arias-Aranda D, Castro JL, Navarro M, Zurita JM (2009) A cbr system for knowing the relationship between flexibility and operations strategy. In: Proceedings of the 18th international symposium on foundations of intelligent systems, ISMIS’09, pp 463–472Ates C (2004) Prediction markets are only human: subadditivity in probability judgments. In: MSC in finance and international businessBerlemann M, Schmidt C (2001) Predictive accuracy of political stock markets—empirical evidence from a European perspective. Technical report 2001-57Betfair (2009) http://www.betfaircorporate.comChen Y, Goel S, Pennock D (2008) Pricing combinatorial markets for tournaments. In: STOC’08: proceedings of the 40th annual ACM symposium on theory of computing. ACM Press, New York, pp 305–314Debnath S, Pennock DM, Giles CL, Lawrence S (2003) Information incorporation in online in-game sports betting markets. In: Proceedings of the 4th ACM conference on electronic commerce, EC ’03. ACM Press, New York, pp 258–259. doi: 10.1145/779928.779987Fischoff B, Slovic P, Lichtenstein S (1977) Knowing with certainty: the appropriateness of extreme confidence. J Exp Psychol Human Percept Perform 3:552–564Forsythe R, Rietz T, Ross T (1999) Wishes, expectations and actions: a survey on price formation in election stock markets. J Econ Behav Organ 39(1):83–110Fortnow L, Kilian J, Pennock DM, Wellman MP (2005) Betting Boolean-style: a framework for trading in securities based on logical formulas. Decis Support Syst 39(1):87–104. doi: 10.1016/j.dss.2004.08.010Gayer G (2010) Perception of probabilities in situations of risk: a case based approach. Games Econ Behav 68(1):130–143Guo M, Pennock D (2009) Combinatorial prediction markets for event hierarchies. In: Proc of the 8th AAMAS’09. Int foundation for autonomous agents and multiagent systems, pp 201–208Huang W, Lai K, Nakamori Y, Wang S (2004) Forecasting foreign exchange rates with artificial neural networks: a review. Int J Inf Technol Decis Mak 3(1):145–165Hüllermeier E (2007) Case-based approximate reasoning. Theory and decision library, vol 44. Springer, BerlinKim K-J, Ahn H (2012) Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36(4):887–898LeBaron B (1998) Agent based computational finance: suggested readings and early research. J Econ Dyn ControlLiu Y, Yang C, Yang Y, Lin F, Du X, Ito T (2012) Case learning for cbr-based collision avoidance systems. Appl Intell 36(2):308–319Love BC (2008) Behavioural finance and sports betting markets. In: MSC in finance and international businessLuque C, Valls JM, Isasi P (2011) Time series prediction evolving Voronoi regions. Appl Intell 34(1):116–126Mantaras RLD, McSherry D, Bridge D, Leake D, Smyth B, Craw S, Faltings B, Maher M, Lou C, Forbus MCK, Keane M, Aamodt A, Watson I (2005) Retrieval, reuse, revision and retention in case-based reasoning. Knowl Eng Rev 20(3):215–240Moody J (1995) Economic forecasting: challenges and neural network solutions. In: Proceedings of the international symposium on artificial neural networksOntañón S, Plaza E (2009) Argumentation-based information exchange in prediction markets. Argument Multi-Agent Syst 5384:181–196Ontañón S, Plaza E (2011) An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems. Multiagent Grid Syst 7:95–108Palmer R, Arthur W, Holland J, Lebaron B, Tayler P (1994) Artificial economic life: a simple model of a stock market. Physica D 75:264–274Pennock D, Debnath S, Glover E, Giles C (2002) Modelling information incorporation in markets, with application to detecting and explaining events. In: Proceedings of the 18th annual conference on uncertainty in artificial intelligence (UAI-02), San Francisco, CA. Morgan Kaufmann, San Mateo, pp 404–405Pennock DM, Lawrence S, Nielsen FÅ, Giles CL (2001) Extracting collective probabilistic forecasts from web games. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’01. ACM Press, New York, pp 174–183. doi: 10.1145/502512.502537Plott CR (2000) Markets as information gathering tools. South Econ J 67(1):2–15Qian B, Rasheed K (2007) Stock market prediction with multiple classifiers. Appl Intell 26(1):25–33Raudys S, Zliobaite I (2006) The multi-agent system for prediction of financial time series. In: ICAISC, vol 4029. Springer, Berlin, pp 653–662Schmidt C, Werwatz A (2002) How accurate do markets predict the outcome of an event? The euro 2000 soccer championship experiment, 2002-09. Max Planck Institute of Economics, Strategic Interaction Group. http://ideas.repec.org/p/esi/discus/2002-09.htmlShiu SCK, Pal SK (2004) Case-based reasoning: concepts, features and soft computing. Appl Intell 21(3):233–238Wellman MP, Reeves DM, Lochner KM, Vorobeychik Y (2004) Price prediction in a trading agent competition. J Artif Intell Res 21:19–3

Artificial intelligence tools for academic management: assigning students to academic supervisors

[EN] In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.Sanchez-Anguix, V.; Chalumuri, R.; Alberola Oltra, JM.; Aydogan, R. (2020). Artificial intelligence tools for academic management: assigning students to academic supervisors. IATED. 4638-4644. https://doi.org/10.21125/inted.2020.1284S4638464

Multi-dimensional adaptation in MAS organizations

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Organization adaptation requires determining the consequences of applying changes not only in terms of the benefits provided but also measuring the adaptation costs as well as the impact that these changes have on all of the components of the organization. In this paper, we provide an approach for adaptation in multiagent systems based on a multidimensional transition deliberation mechanism (MTDM). This approach considers transitions in multiple dimensions and is aimed at obtaining the adaptation with the highest potential for improvement in utility based on the costs of adaptation. The approach provides an accurate measurement of the impact of the adaptation since it determines the organization that is to be transitioned to as well as the changes required to carry out this transition. We show an example of adaptation in a service provider network environment in order to demonstrate that the measurement of the adaptation consequences taken by the MTDM improves the organization performance more than the other approaches.Manuscript received January 2, 2012; revised July 26, 2012; accepted August 7, 2012. Date of publication August 31, 2012; date of current version April 16, 2013. This work was supported in part by projects TIN2008-04446 and TIN2009-13839-C03-01. J. M. Alberola received a Grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289). This paper was recommended by Associate Editor J. Huang.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2013). Multi-dimensional adaptation in MAS organizations. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 43(2):622-633. https://doi.org/10.1109/TSMCB.2012.2213592S62263343

Feedback Efectivo en Prácticas de Programación

Las asignaturas de carácter práctico como la programación, presentan históricamente un alto índice de abandonos y unas tastas de aprobados bajas. Una característica de estas asignaturas es que el material que se aprende, necesita ser afianzado para aprender nuevos conceptos, por lo tanto, un feedback progresivo y continuo es esencial para la motivación de los alumnos. En este artículo, presentamos una experiencia docente que obtiene dicho feedback mediante el uso de la plataforma educativa. El impacto a diferentes niveles de esta experiencia es analizado en un grupo de alumnos.Alberola Oltra, JM.; García Fornes, AM. (2013). Feedback Efectivo en Prácticas de Programación. VAEP-RITA. Versión Abierta Español-Portugués. 1(2):88-96. http://hdl.handle.net/10251/60536S88961

A board game-based virtual environment for intelligent bots programming,

[EN] Nowadays, there are few virtual environments based on board games with a didactic purpose. In fact, a new board game-based environment is rarely created for training bots unless it is necessary for a study. However, the development of intelligent bots applied to such games would be a stimulus to motivate disciplines such as programming or Artificial Intelligence. In this paper, we present a virtual environment based on a well-known board game such as Catan, which allows the incorporation of bots that can play against each other. In this sense, the virtual environment allows the development of new bots with their respective own strategies and algorithms, so that simulations of games can be carried out to measure their effectiveness. In addition, it also allows the simulation of multiple games to develop bots that incorporate learning techniques based on Artificial Intelligence or Machine Learning. In this sense, the virtual environment offers a very interesting tool to be used in subjects related to these disciplinesHeras, A.; Sanchez-Anguix, V.; Alberola Oltra, JM.; Pérez Pascual, MA. (2023). A board game-based virtual environment for intelligent bots programming,. IATED. 1-7. https://doi.org/10.21125/inted.2023.08511

Metrics for privacy assessment when sharing information in online social networks

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498.Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723S143631143645

A Scalable Multiagent Platform for Large Systems

[EN] A new generation of open and dynamic systems requires execution frameworks that are capable of being efficient and scalable when large populations of agents are launched. These frameworks must provide efficient support for systems of this kind, by means of an efficient messaging service, agent group management, security issues, etc. To cope with these requirements, in this paper, we present a novel Multiagent Platform that has been developed at the Operating System level. This feature provides high efficiency rates and scalability compared to other high-performance middleware-based Multiagent Platforms.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and projects TIN2011-27652-C03-01 and TIN2008-04446. Juan M. Alberola has received a grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289).Alberola Oltra, JM.; Such Aparicio, JM.; Botti, V.; Espinosa Minguet, AR.; García-Fornes, A. (2013). A Scalable Multiagent Platform for Large Systems. Computer Science and Information Systems. 10(1):51-77. doi:10.2298/CSIS111029039AS517710

ICT tools for tackling bullying in schools: an analysis and opportunities

[EN] Bullying is defined as the act of repeatedly and intentionally causing harm to another person who feels helpless (i.e., the victim) against the perpetrator or group of perpetrators. These series of acts of physical and/or psychological violence have been reported to cause a negative impact on the victim on several aspects of his/her well-being and daily life. Recently, multidisciplinary research teams have noticed the potential of ICT tools as catalysts for fighting bullying in schools. A range of ICT-based tools such as elearning systems, intelligent tutoring systems, gamified applications, analytics, or even artificial intelligence have been proposed as technological assets against bullying. In this paper, we identify, categorize, and analyse the use of these ICT tools against bullying under a wide range of criteria such as their role (e.g., prevention, mitigation, detection, etc.), the potential investment that is required, the target age of users, and strengths and weaknesses of each tool. Then, we discuss on potential areas of expansion for the use of ICT tools in the fight bullying, and we identify new potential areas of research.This work has been partially funded by the Generalitat Valenciana (GV/2019/012).Alberola Oltra, JM.; Sanchez-Anguix, V.; Soto-González, MD.; Molines Borrás, S.; Monfort Torres, G.; Díaz Novillo, S. (2020). ICT tools for tackling bullying in schools: an analysis and opportunities. IATED. 4662-4667. https://doi.org/10.21125/inted.2020.1289S4662466

Supporting Dynamicity in Emergency Response Applications

[EN] Multiagent Systems are a promising paradigm for software development. It is feasible to model such systems with many components where each one can solve a specific problem. This division of responsibilities allows multiagent systems to work in dynamically changing environments. An example of an environment that is very changeable is related with emergencies management. Emergency management systems depend on the cooperation of all their components due to their specialization. In order to obtain this cooperation, the components need to interact with each other and adapt their interactions depending on their purpose and the system components they are interacting with. Also, new components may arrive on the scene, which must be informed about the interaction policies that original components are using. Although Multiagent Systems are suited to managing scenarios of this kind, their effectiveness depends on their capacity to dynamically modify and adapt the protocols that control the interactions among agents in the system. In this paper, an infrastructure to support dynamically changing interaction protocols is presented.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and project TIN2008-04446.López Fogués, R.; Such Aparicio, JM.; Alberola Oltra, JM.; Espinosa Minguet, AR.; García Fornes, AM. (2014). Supporting Dynamicity in Emergency Response Applications. Computing and Informatics. 33(6):1288-1311. http://hdl.handle.net/10251/50972S1288131133