Adapting Agent Platforms to Web Service Environments

This master thesis tries to address the above-mentioned issues by creating an agent plat- form suitable for encapsulating web-services into agents, providing them with typical agent capabilities (such as learning or complex communication and reasoning mechanisms). The objective of this point is to create a generic, modular agent platform that is able to run lightweight agents. The agents should be able to easily invoke web-services, e ectively encapsulating them. They also should be able to easily coordinate for composing the invoked services in order to perform complex tasks. Thus, the platform must provide facilities to allow the agents perform these service invocations

Generalizations of dung frameworks and their role in formal argumentation

This article provides a short survey of some of the most popular abstract argumentation frameworks available today. The authors present the general idea of abstract argumentation, highlighting the role of abstract frameworks in the argumentation process, and review the original Dung frameworks and their semantics. A discussion of generalizations of these frameworks follows, focusing on structures taking preferences and values into account and approaches in which not only attack but also support relations can be modeled. Finally, the authors review the concept of abstract dialectical frameworks, one of the most general systems for abstract argumentation providing a flexible, principled representation of arbitrary argument relations

Les systèmes d'argumentation basés sur les préférences : application à la décision et à la négociation

L'argumentation est considérée comme un modèle de raisonnement basé sur la construction et l'évaluation d'arguments. Ces derniers sont sensés soutenir/expliquer/attaquer des assertions qui peuvent être des décisions, des avis, etc... Cette thèse contient trois parties. La première concerne la notion d'équivalence de systèmes d'argumentation. Nous avons proposé différents critères d'équivalence, étudié leurs liens et montré sous quelles conditions deux systèmes sont équivalents selon les critères proposés. La notion d'équivalence est ensuite utilisée pour calculer les noyaux d'un système d'argumentation. Un noyau est un sous-système fini d'un système d'argumentation et équivalent à celui-ci. La deuxième partie de la thèse concerne l'utilisation des préférences dans l'argumentation. Nous avons étudié les rôles que les préférences peuvent jouer dans un système d'argumentation. Deux rôles particuliers ont été identifiés. Nous avons montré que les travaux existant ont abordé seulement le premier rôle et les approches proposées peuvent retourner des résultats contre-intuitifs lorsque la relation d'attaque entre arguments n'est pas symétrique. Nous avons développé une approche qui pallie ces limites. La troisième partie applique notre modèle d'argumentation à la décision et à la négociation. Nous avons proposé une instanciation de notre modèle pour la décision argumentée. Puis, nous avons étudié la dynamique de cette instanciation. Plus précisément, nous avons montré comment le statut des options change à la lumière d'un nouvel argument. Nous avons également employé notre modèle afin de montrer les avantages de l'argumentation dans des dialogues de négociation.Argumentation is a promising approach for reasoning with uncertain or incoherent knowledge or more generally with common sense knowledge. It consists of constructing arguments and counter-arguments, comparing the different arguments and selecting the most acceptable among them. This thesis contains three parts. The first one concerns the notion of equivalence between two argumentation frameworks. We studied two families of equivalence: basic equivalence and strong equivalence. We proposed different equivalence criteria, investigated their links and showed under which conditions two frameworks are equivalent w.r.t. each of the proposed criteria. The notion of equivalence is then used in order to compute the core(s) of an argumentation framework. A core of a framework is its compact version, i.e. an equivalent sub-framework. The second part of the thesis concerns the use of preferences in argumentation. We investigated the roles that preferences may play in an argumentation framework. Two particular roles were identified. Besides, we showed that almost all the existing works have tackled only the first role. Moreover, the proposed approaches suffer from a drawback which consists of returning conflicting extensions. We proposed a general approach which solves this problem and takes into account both roles of preferences. The third part illustrates our preference-based argumentation frameworks (PAF) in case of decision making and negotiation. We proposed an instantiation of our PAF which rank-orders options in a decision making problem and studied the dynamics of this model. We also used our PAF in order to show the benefits of arguing in negotiation dialogues

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Politics in robes? The European Court of Justice and the myth of judicial activism

What characterizes the EU today is that it is not only a multi-level governance system, but also a multi-context system. The making of Europe does not just take place on different levels within the European political framework, executed by different groups of actors or institutions. Rather, it also happens in different and distinguishable social contexts - distinct functional, historical, and local frameworks of reasoning and action - that political science alone cannot sufficiently analyze with conventional and generalizing models of explanation. The European law is such a context, and it should be perceived as a self-contained sphere of argument and action that generates impetus for integration. Therefore, the role of the European Court of Justice in the process of integration may only be adequately captured by examining European law as an independent space of reasoning and action. --European Court of Justice (ECJ),Integration through Law,Integration Theory,Regional Integration,Rationalism,Trivial Rationalism,Context Rationality,Context of Law,Context Analysis,Judicial Politics

Supporting argumentation dialogues in group decision support systems: an approach based on dynamic clustering

Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect-based sentiment analysis (ABSA) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.This research was funded by National Funds through the Portuguese FCT-Fundacao para a Ciencia e a Tecnologia under the R&D Units Project Scope UIDB/00319/2020, UIDB/00760/2020, UIDP/00760/2020, and by the Luis Conceicao Ph.D. Grant with the reference SFRH/BD/137150/2018

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem