Angeles Manjarrés

We discuss the use of the UML to describe “Analysis Patterns” in AI, an area where OAD techniques are not widely used, in spite of the fact that some of the inspiration for the object approach can be traced to developments in this area. We study the relation between the notion of analysis pattern in the context of OO software development methods, and that of Generic Task in AI software development methods such as CommonKADS. Our interest is motivated by the belief that in the analysis and design of certain AI applications, particularly in Distributed AI, OO style patterns may be more appropriate than Generic Tasks. To illustrate the relation between these concepts, we provide a UML description of the heuristic multiattribute decision pattern, a corresponding Generic Task having already been proposed in the literature. We illustrate the wide applicability of this pattern by specialising it to obtain a therapy decision pattern. We discuss the suitability of the UML, together with OCL, for describing this and other analysis patterns arising in AI.

ABSTRACT In this paper we introduce the MAMIPEC project. The aim of this project is to explore the application of affective computing to accessible and personalized learning systems. To this end, we consider a user context which includes appliances and devices to enrich user interaction. We describe the research objectives and present on-going work towards understanding the affective support needed in educational scenarios.

A criticism that could be made of the most commonly-used AI development methodologies, in particular that based on the widely-known KADS-CommonKADS expertise model, is the unsuitability of the available techniques for specifying and reusing knowledge-model patterns. In this article, we investigate the alternative of formulating knowledge model patterns as OO analysis patterns. We seek to take advantage of research on design pattern specification, aimed at modelling patterns by means of structural and behavioural “metalevel” constraints. We illustrate our argument with the formulation of an OO “assessment pattern”.

"In this short article, we argue that the SDGs should figure more prominently in marking out the route to realising the potential of A/IS, while at the same time recognizing the need for a critical appraisal. We discuss how increasing awareness of an interdisciplinary research field "A/IS for development" (A/IS4D) could contribute to achieving this, where by this term, we refer to a field that deals with the distinctive challenges posed by A/IS technologies, particularly for more vulnerable and marginalized populations, while integrating a development studies, human-rights centered perspective. We contend that such a perspective, and more specifically, the measuring of impact in terms of appropriate SDGs, is critical to avoiding possible adverse effects arising from the introduction of such a disruptive technology without sufficient reflection, a real and present danger given the current techno-optimist zeitgeist and the enormous power of the Information and Communication Technologies (ICT) corporate sector. Unfortunately, the academic research literature of this field, like that of the more general and well-established ICT4D field, remains marginal, with contributions having little representation in high-impact conferences and journals. This short article constitutes a modest attempt to contribute to the dissemination of this important topic among researchers and professionals in the A/IS sector, in the way of a call for action. "

We propose a semi-automatic method for the generation of educational-competency maps from repositories of multiple-choice question responses, using Bayesian structural learning and data-mining techniques. We tested our method on a large repository of responses to multiple-choice exam questions from an undergraduate course in Languages and Automata Theory at Spain’s national distance-learning university (UNED). We also draw up guidelines and best practices, with a view to defining an educational data-mining methodology and to contributing to the development of educational data-mining tools.

<bold>The United Nations</bold> (UN) 2030 Agenda and other movements toward setting global goals such as the Paris Agreement and the European Green Deal/U.S. Green New Deal are laying the groundwork for a transformation beyond purely market-based economics toward sustainability and inclusiveness <xref ref-type="bibr" rid="ref1">[1]</xref>, in which technological innovation and, in particular, artificial intelligence (AI) can play a central role. The European Union (EU) is committed to the 2030 Agenda and the sustainable development goals (SDGs), which the UN itself has recognized cannot be achieved without a people-focused, science-based, digital revolution <xref ref-type="bibr" rid="ref2">[2]</xref>. This commitment to the 2030 Agenda should entail promoting an inclusive and sustainable AI strategy, rather than a strategy with a narrow focus on competitiveness <xref ref-type="bibr" rid="ref3">[3]</xref>, <xref ref-type="bibr" rid="ref4">[4]</xref>. In order for AI to contribute to achieving the SDGs, a systemic approach to the development of AI solutions is required <xref ref-type="bibr" rid="ref5">[5]</xref>–<xref ref-type="bibr" rid="ref6"/><xref ref-type="bibr" rid="ref7"/><xref ref-type="bibr" rid="ref8"/><xref ref-type="bibr" rid="ref9">[9]</xref>. Conversely, the SDGs provide an ideal framework to test the desirability of AI solutions <xref ref-type="bibr" rid="ref10">[10]</xref>. Europe’s multicultural character and its framework of international collaboration give it a head start toward becoming a global reference in the promotion of an inclusive and sustainable AI. Sharing the experiences and practices of such a European AI could make a significant contribution to achieving the SDGs.

We discuss the use of the UML to describe “Analysis Patterns” in AI, an area where OAD techniques are not widely used, in spite of the fact that some of the inspiration for the object approach can be traced to developments in this area. We study the relation between the notion of analysis pattern in the context of OO software development methods, and that of Generic Task in AI software development methods such as CommonKADS. Our interest is motivated by the belief that in the analysis and design of certain AI applications, particularly in Distributed AI, OO style patterns may be more appropriate than Generic Tasks. To illustrate the relation between these concepts, we provide a UML description of the heuristic multiattribute decision pattern, a corresponding Generic Task having already been proposed in the literature. We illustrate the wide applicability of this pattern by specialising it to obtain a therapy decision pattern. We discuss the suitability of the UML, together with OCL, for describing this and other analysis patterns arising in AI.

... as structural aspects of OO models and should therefore be included in the structural representation of the pattern, making use of ... International Conference on Knowledge Based Systems and Applied Artificial In-telligence: Research and Development in Intelligent Systems ...

In this article, we investigate the use of the OO computational paradigm for the formulation of knowledge model patterns as OO analysis patterns. We seek to take advantage of research on design pattern specification, aimed at modelling patterns by means of structural and behavioural "meta-level" constraints, introducing appropriate modifications into the UML. We illustrate our argument with the formulation of an OO "assessment pattern" in analogy to the well known "assessment task template".

En este artículo presentamos las potenciales contribuciones de la Inteligencia Artificial (IA) a un desarrollo sostenible y equitativo, respetuoso con los derechos humanos, frente a sus potenciales riesgos. Argumentamos cómo un enfoque particular de la I+D, y por ende de la educación de los futuros profesionales, podría conferir a esta tecnología un papel decisivo en la consecución de los Objetivos de Desarrollo Sostenible de Naciones Unidas. En particular, proponemos la metodología del Aprendizaje-Servicio como instrumento pedagógico para la educación de ingenieros de la tecnología inteligente comprometidos con la Agenda 2030. Ilustramos también nuestra experiencia de Aprendizaje-Servicio virtual en el contexto de un proyecto de innovación docente impulsado por el grupo COETIC ("Innovación docente para el desarrollo de competencias éticas y cívicas, y metodologías comunitarias en educación superior") de la Universidad Nacional de Educación a Distancia de España (UNED). En...

<bold>The last few</bold> years have seen a large number of initiatives on artificial intelligence (AI) ethics: intergovernmental-institution initiatives such as “Ethics Guidelines for Trustworthy AI” from the high-level expert group on AI of the European Commission <xref ref-type="bibr" rid="ref1">[1]</xref> or the Organisation for Economic Cooperation and Development (OECD) Council Recommendation on Artificial Intelligence <xref ref-type="bibr" rid="ref2">[2]</xref>, government initiatives such as that of the U.K. Parliament Select Committee on Artificial Intelligence <xref ref-type="bibr" rid="ref3">[3]</xref>, industry initiatives on AI ethical codes such as those of Google, IBM, Microsoft, and Intel, academic initiatives such as the Montreal declaration for the responsible development of AI <xref ref-type="bibr" rid="ref4">[4]</xref>, the Stanford University 100 Year Study on AI <xref ref-type="bibr" rid="ref5">[5]</xref> or the Alan Turing Institute’s “Understanding Artificial Intelligence Ethics and Safety” <xref ref-type="bibr" rid="ref6">[6]</xref>, and finally professional body initiatives such as the IEEE Global Initiative on Ethics of Autonomous/Intelligent Systems (A/IS) <xref ref-type="bibr" rid="ref7">[7]</xref>. These initiatives, while acknowledging the potential of A/IS technologies to contribute to global socioeconomic solutions, highlight the increasing challenges posed by these technologies in the ethical, moral, legal, humanitarian, and sociopolitical domains.

In this paper, we report on the research results concerning the potential of e-learning for global and integral education in the EEES context, based on experience with the "Artificial Intelligence (AI) applications for human and sustainable development" module, part of the "Advanced AI" international master of the Spanish distance-learning university (UNED). This multi-disciplinary module has been designed with the aim of giving the students ─ future AI technology developers and researchers ─ a wide and humanistic perspective on their specialisation area by addressing its global context: philosophical, anthropological, ethical and socio-political. In this way, the students acquire a deeper knowledge of the subjects they study in other modules of the master, a knowledge that resonates in other dimensions of their personal development path. The final objective is to educate the students for an ethical exercise of their profession in coherence with a human and susta...