This study explores the integration of the JADE (Java Agent Development Framework) platform with deep reinforcement learning (DRL) to enhance governmental interoperability and optimize administrative workflows in municipal settings. The proposed approach combines the JADE’s robust multi-agent system (MAS) capabilities with the adaptive decision-making power of DRL to address prevalent challenges faced by government agencies, such as fragmented operations, incompatible data formats, and rigid communication protocols. By enabling seamless communication between agents across departments such as the Treasury, the Event Management department, and the Public Safety department, the hybrid system fosters real-time collaboration and supports efficient, data-driven decision making. Agents leverage historical and real-time data to adapt to environmental changes and make optimized decisions that align with overarching governmental objectives, such as resource allocation and emergency response. The result is a system capable of managing intricate administrative duties using structured agent communication and the integration of DRL-driven learning models, improving governmental interoperability. Key performance indicators highlight the system’s effectiveness, achieving a task completion rate of 95%, decision accuracy of 96%, and a communication latency of just 120 ms. Additionally, the framework’s flexibility ensures seamless scalability, accommodating complex and large-scale tasks across multiple governmental units. This research presents a scalable, automated, and resilient framework for optimizing governmental processes, offering a pathway to more efficient, transparent, and adaptive public sector operations.

In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. This is exacerbated in public administrations, where knowledge should be oriented towards public services. In this study, an artificial intelligence-based method for the evaluation of knowledge is described. This method takes advantage of the structure and contents of the knowledge representation schemas (representing the knowledge of the corresponding experts) to carry out knowledge evaluation. More precisely, the method allows the various comparisons between the schemas to be integrated and the overall schema to evaluate the contribution of each schema.

There are more and more Artificial Intelligence (AI)-based systems that are based on the analysis of observed behavior of, and knowledge on, the environment. AI algorithms in the context of the environmental management domain allow for data collection and processing to design action patterns for diverse activities in such a domain. This chapter presents a machine-readable knowledge model framework to classify environmental problems arising in water management environments from various analytical perspectives. In addition, new AI methods are proposed to automatically evaluate and integrate knowledge in this domain. <!----> <!----> <!----> <!---->

Se ha evaluado el Portal de Archivos Españoles (PARES) y su sistema de búsqueda y recuperación. Se trata de una plataforma común que integra las bases de datos de varios archivos de carácter histórico nacional. Para ello se definieron previamente una serie de parámetros e indicadores que nos ofrecen información adicional sobre este portal, y así conocer si alcanza el nivel suficiente o bien llega a la excelencia. Llegamos a la conclusión de que el portal debe adecuarse mejor a sus usuarios en lo referente a formación (para poder acceder y recuperar la documentación necesaria) e información (con el fin de orientar al usuario para que se sienta cómodo en la utilización del portal). A pesar de algunos fallos, el proyecto Pares supone un gran avance en el acceso y localización de la documentación de carácter histórico en España.

El proceso de transformación tecnológica está provocando la necesidad de redirigir la gestión interna de las Administraciones públicas y sus relaciones con la ciudadanía, atendiendo en todo caso a los principios de seguridad, interoperabilidad y accesibilidad a nivel global, donde la verdadera calidad de las Administraciones públicas radica en la gestión del conocimiento para la mejora de los servicios electrónicos y en la alfabetización de la ciudadanía para hacer un buen uso de ellos. En este trabajo se describe un método basado en modelos integradores y una encuesta realizada a 630 ciudadanos/as, que ha servido para identificar el grado de conocimiento, percepción y uso que la ciudadanía tiene de la Administración electrónica frente a la tramitación de un proceso administrativo por medios electrónicos. Los datos se analizan mediante el método de análisis de correlación para establecer relaciones entre variables, aplicando el coeficiente de correlación Chi-cuadrado de Pearson y el análisis de estadísticos descriptivos. El modelo proporciona mecanismos de ajuste para mejorar su alineación con las directivas nacionales e internacionales sobre Administración electrónica y gobierno electrónico. ARK CAICYT: http://id.caicyt.gov.ar/ark:/s18511740/bn39nlt06

Safety issues are relevant in the interaction of human and robotic systems, as these might be a risk for humans' physical integrity. Robotic systems in the armies are a good example of such risk, since these systems are highly dependent on the software integrity that governs their behaviour. But in civil society there are examples of such issues as well. Also, medical applications are usually designed to be very accurate/precise, for instance, in surgery. If that accuracy/precision is threatened due to a cyberattack—as some of those robots are connected to communication networks—the patient could even die. In this chapter, the authors describe a modelling approach that categorizes the most relevant cybersecurity threats to robots from different analytical viewpoints. To illustrate the usefulness of this model, the main characteristics of a model addressing the cybersecurity threats to manufacturing robotic systems is shown in this work. Such a model has been implemented into machine-understandable software by using standard knowledge representation methods. Request access from your librarian to read this chapter's full text.