A Conviviality Measure for Early Requirement Phase

In this paper, we consider the design of convivial multi-agent systems. Conviviality has recently been proposed as a social concept to develop multi-agent systems. In this paper we introduce temporal dependence networks to model the evolution of dependence networks and conviviality over time, we introduce epistemic dependence networks to combine the viewpoints of stakeholders, and we introduce normative dependence networks to model the transformation of social dependencies by hiding power relations and social structures to facilitate social interactions. We show how to use these visual languages in design, and we illustrate the design method using an example on virtual children adoptions

09121 Abstracts Collection -- Normative Multi-Agent Systems

From 15.03. to 20.03.2009, the Dagstuhl Seminar 09121 ``Normative Multi-Agent Systems \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

Cooperation in Multi-Agent Reinforcement Learning

As progress in reinforcement learning (RL) gives rise to increasingly general and powerful artificial intelligence, society needs to anticipate a possible future in which multiple RL agents must learn and interact in a shared multi-agent environment. When a single principal has oversight of the multi-agent system, how should agents learn to cooperate via centralized training to achieve individual and global objectives? When agents belong to self-interested principals with imperfectly-aligned objectives, how can cooperation emerge from fully-decentralized learning? This dissertation addresses both questions by proposing novel methods for multi-agent reinforcement learning (MARL) and demonstrating the empirical effectiveness of these methods in high-dimensional simulated environments. To address the first case, we propose new algorithms for fully-cooperative MARL in the paradigm of centralized training with decentralized execution. Firstly, we propose a method based on multi-agent curriculum learning and multi-agent credit assignment to address the setting where global optimality is defined as the attainment of all individual goals. Secondly, we propose a hierarchical MARL algorithm to discover and learn interpretable and useful skills for a multi-agent team to optimize a single team objective. Extensive experiments with ablations show the strengths of our approaches over state-of-the-art baselines. To address the second case, we propose learning algorithms to attain cooperation within a population of self-interested RL agents. We propose the design of a new agent who is equipped with the new ability to incentivize other RL agents and explicitly account for the other agents' learning process. This agent overcomes the challenging limitation of fully-decentralized training and generates emergent cooperation in difficult social dilemmas. Then, we extend and apply this technique to the problem of incentive design, where a central incentive designer explicitly optimizes a global objective only by intervening on the rewards of a population of independent RL agents. Experiments on the problem of optimal taxation in a simulated market economy demonstrate the effectiveness of this approach.Ph.D

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems

Preference extraction and reasoning in negotiation dialogues

Modéliser les préférences des utilisateurs est incontournable dans de nombreux problèmes de la vie courante, que ce soit pour la prise de décision individuelle ou collective ou le raisonnement stratégique par exemple. Cependant, il n'est pas facile de travailler avec les préférences. Comme les agents ne connaissent pas complètement leurs préférences à l'avance, nous avons seulement deux moyens de les déterminer pour pouvoir raisonner ensuite : nous pouvons les inférer soit de ce que les agents disent, soit de leurs actions non-linguistiques. Plusieurs méthodes ont été proposées en Intelligence Artificielle pour apprendre les préférences à partir d'actions non-linguistiques mais à notre connaissance très peu de travaux ont étudié comment éliciter efficacement les préférences verbalisées par les utilisateurs grâce à des méthodes de Traitement Automatique des Langues (TAL).Dans ce travail, nous proposons une nouvelle approche pour extraire et raisonner sur les préférences exprimées dans des dialogues de négociation. Après avoir extrait les préférences de chaque tour de dialogue, nous utilisons la structure discursive pour suivre leur évolution au fur et à mesure de la conversation. Nous utilisons les CP-nets, un modèle de représentation des préférences, pour formaliser et raisonner sur ces préférences extraites. Cette méthode est d'abord évaluée sur différents corpus de négociation pour lesquels les résultats montrent que la méthode est prometteuse. Nous l'appliquons ensuite dans sa globalité avec des raisonnements issus de la Théorie des Jeux pour prédire les échanges effectués, ou non, dans le jeu de marchandage Les Colons de Catane. Les résultats obtenus montrent des prédictions significativement meilleures que celles de quatre baselines qui ne gèrent pas correctement le raisonnement stratégique. Cette thèse présente donc une nouvelle approche à la croisée de plusieurs domaines : le Traitement Automatique des Langues (pour l'extraction automatique des préférences et le raisonnement sur leur verbalisation), l'Intelligence Artificielle (pour la modélisation et le raisonnement sur les préférences extraites) et la Théorie des Jeux (pour la prédiction des actions stratégiques dans un jeu de marchandage)Modelling user preferences is crucial in many real-life problems, ranging from individual and collective decision-making to strategic interactions between agents for example. But handling preferences is not easy. Since agents don't come with their preferences transparently given in advance, we have only two means to determine what they are if we wish to exploit them in reasoning: we can infer them from what an agent says or from his nonlinguistic actions. Preference acquisition from nonlinguistic actions has been wildly studied within the Artificial Intelligence community. However, to our knowledge, there has been little work that has so far investigated how preferences can be efficiently elicited from users using Natural Language Processing (NLP) techniques. In this work, we propose a new approach to extract and reason on preferences expressed in negotiation dialogues. After having extracted the preferences expressed in each dialogue turn, we use the discursive structure to follow their evolution as the dialogue progresses. We use CP-nets, a model used for the representation of preferences, to formalize and reason about these extracted preferences. The method is first evaluated on different negotiation corpora for which we obtain promising results. We then apply the end-to-end method with principles from Game Theory to predict trades in the win-lose game The Settlers of Catan. Our method shows good results, beating baselines that don't adequately track or reason about preferences. This work thus presents a new approach at the intersection of several research domains: Natural Language Processing (for the automatic preference extraction and the reasoning on their verbalisation), Artificial Intelligence (for the modelling and reasoning on the extracted preferences) and Game Theory (for strategic action prediction in a bargaining game

New Studies and Research in Social Sciences

This study focuses on the “The Bureaucratic Personality” view through the three prominent woman authors’, who lived in a republican period of Turkey, bureaucratic types are indicated in their literary works. The focused questions are following: 1) How bureaucrats characteristics can be explained in Bureaucratic Personality view? 2) How can be clarified bureaucratic types within the three woman authors’, who lived in republican period, literary works? I have selected the following literary works in considering the research question: Şukufe Nihal "Yalnız Dönüyorum" Halide Edip Adıvar "Zeyno'nun Oğlu" and Halide Nusret "Gül'ün Babası Kim". In questioned literary works are noted as the data set. Bureaucrats’ characteristic and their manner for cases are noticed as indicators, in methodology, this paper is a theoric, exploration study. The study designed into three sections. In the first part Merton, Hummel, and Argrys studies are referenced as Bureaucratic Personality” theories, likewise, bureaucrats’ characteristics are explained. In the next section, bureaucratic types are noted within the Şukufe Nihal "Yalnız Dönüyorum", Halide Edip Adıvar "Zeyno'nun Oğlu", and Halide Nusret Zorlutuna "Gül'ün Babası Kim" literary works. In a similar vein, bureaucrats characteristic and their manner for cases are remarked. In the last part, bureaucratic types are analyzed through “The Bureaucratic Personality” view