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. 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Analyzing the effect of gain time on soft task scheduling policies in real-time systems

In hard real-time systems, gain time is defined as the difference between the Worst Case Execution Time (WCET) of a hard task and its actual processor consumption at runtime. This paper presents the results of an empirical study about how the presence of a significant amount of gain time in a hard real-time system questions the advantages of using the most representative scheduling algorithms or policies for aperiodic or soft tasks in fixed-priority preemptive systems. The work presented here refines and complements many other studies in this research area in which such policies have been introduced and compared. This work has been performed by using the authors' testing framework for soft scheduling policies, which produces actual, synthetic, randomly generated applications, executes them in an instrumented Real-Time Operating System (RTOS), and finally processes this information to obtain several statistical outcomes. The results show that, in general, the presence of a significant amount of gain time reduces the performance benefit of the scheduling policies under study when compared to serving the soft tasks in background, which is considered the theoretical worst case. In some cases, this performance benefit is so small that the use of a specific scheduling policy for soft tasks is questionable. © 2012 IEEE.This work is partially funded by research projects PROMETEO/2008/051, CSD2007-022, and TIN2008-04446.Búrdalo Rapa, LA.; Terrasa Barrena, AM.; Espinosa Minguet, AR.; García Fornes, AM. (2012). Analyzing the effect of gain time on soft task scheduling policies in real-time systems. IEEE Transactions on Software Engineering. 38(6):1305-1318. https://doi.org/10.1109/TSE.2011.95S1305131838

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

Proceedings of The Third International Workshop on Iinfraestructures and tools for multiagent systems : ITMAS 2012

El taller "Infrastructures and Tools for Multiagent Systems (ITMAS2012)" es un foro de ámbito internacional que actúa como punto de encuentro para especialistas del mundo académico y de la industria que se dedican a trabajar en infraestructuras y herramientas para el diseño, desarrollo, ejecución, gestión, y evaluación de aplicaciones basadas en Sistemas Multiagente. Estas infraestructuras y herramientas juegan un papel fundamental para aplicar las tecnologías de Agentes y Sistemas Multiagente a problemas del mundo real. De hecho, el éxito que puedan llegar a tener las tecnologías de Agentes y Sistemas Multiagente depende en gran medida de que se desarrollen infraestructuras y herramientas que soporten su implementación.ITMAS workshop aims at bringing together leading researchers from both academia and industry to discuss issues on the design and implementation of infrastructures and tools for Multiagent Systems. This includes research on supporting essential features in Multiagent Systems (such as agent organizations, mobility, etc.) and facilitate the system design, management, execution and evaluation. Moreover, in order for Multiagent Systems to be included in real domains such as media and Internet, logistics, e-commerce and health care, infrastructures and tools for Multiagent Systems should provide efficiency, scalability, security, management, monitorization and other features related to building real applicationsBotti Navarro, VJ.; Ricci, A.; García Fornes, AM.; Weyns, D.; Such, JM.; Alberola, JM.; Pechoucek, M. (2012). Proceedings of The Third International Workshop on Iinfraestructures and tools for multiagent systems : ITMAS 2012. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/16889Archivo delegad

CONCURRÈNCIA I SISTEMES DISTRIBUÏTS

Aquest llibre proporciona una visió integradora de les aplicacions concurrents i els sistemes distribuïts, descrivint els seus fonaments, tècniques i mètodes més rellevants. També inclou exemples de programes concurrents en llenguatge Java. Així mateix, s'analitzen els mecanismes de sincronització per a sistemes de temps real i s'ofereix una primera aproximació a les tasques d'administració de sistemes (necessàries en sistemes distribuïts).García Fornes, AM.; Espinosa Minguet, AR.; Galdamez Saiz, P.; Argente Villaplana, E.; Sendra Roig, JS.; Muñoz Escoí, FD.; Juan Marín, RD. (2013). CONCURRÈNCIA I SISTEMES DISTRIBUÏTS. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/71992EDITORIA

CONCURRENCIA Y SISTEMAS DISTRIBUIDOS

Este libro proporciona una visión integradora de las aplicaciones concurrentes y los sistemas distribuidos, describiendo sus fundamentos, técnicas y métodos más relevantes. Incluye también ejemplos de programas concurrentes en lenguaje Java. Asimismo, se analizan los mecanismos de sincronización para sistemas de tiempo real y se ofrece una primera aproximación a las tareas de administración de sistemas (necesarias en sistemas distribuidos).Muñoz Escoí, FD.; Argente Villaplana, E.; Espinosa Minguet, AR.; Galdamez Saiz, P.; García Fornes, AM.; Juan Marín, RD.; Sendra Roig, JS. (2013). CONCURRENCIA Y SISTEMAS DISTRIBUIDOS. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/70991EDITORIA

Magentix 2 User's Manual

USER’S MANUAL. Version 2.1.0. January 2015Magentix2 is an agent platform for open Multiagent Systems. Its main objective is to bring agent technology to real domains: business, industry, logistics, e-commerce, health-care, etc. Magentix2 platform is proposed as a continuation of the first Magentix platform. The final goal is to extend the functionalities of Magentix, providing new services and tools to allow the secure and optimized management of open Multiagent Systems. Nowadays, Magentix2 provides support at three levels: - Organization level, technologies and techniques related to agent societies. - Interaction level, technologies and techniques related to communications between agents. - Agent level, technologies and techniques related to individual agents (such as reasoningand learning). Thus, Magentix2 platform uses technologies with the necessary capacity to cope with the dynamism of the system topology and with flexible interactions, which are both natural consequences of the distributed and autonomous nature of its components. In this sense, the platform has been extended in order to support flexible interaction protocols and conversations, indirect communication and interactions among agent organizations. Moreover, other important aspects cover by the Magentix2 project are the security issues.Botti Navarro, VJ.; Argente Villaplana, E.; Alemany Bordera, J.; Bellver Faus, J.; Búrdalo Rapa, LA.; Carrascosa Casamayor, C.; Criado Pacheco, N.... (2015). Magentix 2 User's Manual. http://hdl.handle.net/10251/4845

Subcutaneous anti-COVID-19 hyperimmune immunoglobulin for prevention of disease in asymptomatic individuals with SARS-CoV-2 infection: a double-blind, placebo-controlled, randomised clinical trialResearch in context

Summary: Background: Anti-COVID-19 hyperimmune immunoglobulin (hIG) can provide standardized and controlled antibody content. Data from controlled clinical trials using hIG for the prevention or treatment of COVID-19 outpatients have not been reported. We assessed the safety and efficacy of subcutaneous anti-COVID-19 hyperimmune immunoglobulin 20% (C19-IG20%) compared to placebo in preventing development of symptomatic COVID-19 in asymptomatic individuals with SARS-CoV-2 infection. Methods: We did a multicentre, randomized, double-blind, placebo-controlled trial, in asymptomatic unvaccinated adults (≥18 years of age) with confirmed SARS-CoV-2 infection within 5 days between April 28 and December 27, 2021. Participants were randomly assigned (1:1:1) to receive a blinded subcutaneous infusion of 10 mL with 1 g or 2 g of C19-IG20%, or an equivalent volume of saline as placebo. The primary endpoint was the proportion of participants who remained asymptomatic through day 14 after infusion. Secondary endpoints included the proportion of individuals who required oxygen supplementation, any medically attended visit, hospitalisation, or ICU, and viral load reduction and viral clearance in nasopharyngeal swabs. Safety was assessed as the proportion of patients with adverse events. The trial was terminated early due to a lack of potential benefit in the target population in a planned interim analysis conducted in December 2021. ClinicalTrials.gov registry: NCT04847141. Findings: 461 individuals (mean age 39.6 years [SD 12.8]) were randomized and received the intervention within a mean of 3.1 (SD 1.27) days from a positive SARS-CoV-2 test. In the prespecified modified intention-to-treat analysis that included only participants who received a subcutaneous infusion, the primary outcome occurred in 59.9% (91/152) of participants receiving 1 g C19-IG20%, 64.7% (99/153) receiving 2 g, and 63.5% (99/156) receiving placebo (difference in proportions 1 g C19-IG20% vs. placebo, −3.6%; 95% CI -14.6% to 7.3%, p = 0.53; 2 g C19-IG20% vs placebo, 1.1%; −9.6% to 11.9%, p = 0.85). None of the secondary clinical efficacy endpoints or virological endpoints were significantly different between study groups. Adverse event rate was similar between groups, and no severe or life-threatening adverse events related to investigational product infusion were reported. Interpretation: Our findings suggested that administration of subcutaneous human hyperimmune immunoglobulin C19-IG20% to asymptomatic individuals with SARS-CoV-2 infection was safe but did not prevent development of symptomatic COVID-19. Funding: Grifols