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Artificial agents are usually designed to achieve specific goals. An agent's competency can be defined as its ability to accomplish its goals under different conditions. This thesis restricts attention to a specific type of goal, namely reaching a desired state without exceeding a tolerance threshold of undesirable events in a first-order Markov process. For such goals, the state-dependent competency for an agent can be defined as the probability of reaching the desired state without exceeding the threshold and within a time limit given an initial state. The thesis further defines total competency as the set of state-dependent competency relationships over all possible initial states. The thesis uses a Monte Carlo approach to establish a baseline for estimating state-dependent competency. The Monte Carlo approach (a) uses trajectories sampled from an agent behaving in the environment, and then (b) uses nonlinear regression over the trajectory samples to estimate the competency curve. The thesis further presents an equation demonstrating recurrent relations for total competency and an algorithm based on that equation for computing total competency whose worst case computation time grows quadratically with the size of the state space. Simple maze-based Markov chains show that the Monte Carlo approach to estimating the competency agrees with the results computed by the proposed algorithm. Lastly, the thesis explores a special case where there are multiple sequential atomic goals that make up a complex goal. The thesis models a set of sequential goals as a Bayesian network and presents an equation based on the chain rule for deriving the competency for the complex goal from the competency for atomic goals. Experiments for the canonical taxi problem with sequential goals show the correctness of the Bayesian network-based decomposition approach.

L'essor de l'informatique affective ouvre aujourd'hui la porte à la création de dispositifs artificiels dotés d'une forme d'intelligence sociale et émotionnelle. L’étude de l'interaction homme-machine dans ce contexte offre de nombreuses pistes de recherche. Parmi celles-ci se trouve la question de la personnalité : comment il est possible de modéliser certaines caractéristiques d’une personnalité artificielle ? Comment ces caractéristiques influencent le déroulement de l'interaction entre l'homme et la machine ? Cette question globale soulève plusieurs questions de recherche : Comment définir la personnalité ? Sur quels modèles et théories issus de la psychologie peut-on s'appuyer pour développer une personnalité artificielle ? Quelle méthodologie adopter pour aborder l’implémentation d'un concept psychologique complexe ? Qu'apporte le développement informatique d'une personnalité à l'interaction homme-machine ? Au domaine de la psychologie de la personnalité ? Comment évaluer expérimentalement ces apports ? Pour aborder ces questions, nos travaux se positionnent dans une optique pluridisciplinaire, au croisement de l’informatique et de la psychologie. Au regard de sa pertinence pour une approche computationnelle, nous avons modélisé la régulation du Soi comme une composante de la personnalité. Ce concept est approché à partir de la théorie du regulatory focus. Sur cette base théorique, un cadre de travail conceptuel et un modèle computationnel sont proposés. Un questionnaire mesurant le regulatory focus a également été développé et validé. Ces propositions théoriques sont mises en œuvre dans deux implémentations data-driven (dimensionnelle vs socio-cognitive) dotant des agents de regulatory focus en utilisant de l’apprentissage automatique. Deux études utilisateurs (interaction unique avec agent artificiel vs sessions répétées avec agent animé), présentées dans un cadre ludique, ont permis d’étudier la perception du regulatory focus chez un agent et son impact sur l'interaction. Nos résultats encouragent l’utilisation du regulatory focus en informatique affective et ouvrent des perspectives sur les liens théoriques et méthodologiques entre informatique et psychologie. / The development of affective computing is leading to the design of artificial devices endowed with a form of social and emotional intelligence. The study of human-computer interaction in this context offers many research tracks. Among them is the question of personality: how to model some characteristics of an artificial personality? How these characteristics will influence the course of interaction with users? This goal rises several research questions: how to define personality? On which models and theories from psychology should we rely to define some artificial personality? Which methodology will help to address the implementation of such a complex psychological concept? What could artificial personality bring to the field of human-computer interaction? And to the psychology of personality? How to experimentally evaluate these contributions? To address these issues, this thesis takes a multidisciplinary approach, at the crossing of computing science and psychology. Given its relevance to a computational approach, we modeled self-regulation as a component of personality. This concept is approached from the regulatory focus theory. On this theoretical basis, a conceptual framework and a computational model are proposed. Our theoretical proposals led to two data-driven implementations (dimensional vs. socio-cognitive) which endowed our artificial agents with regulatory focus by using machine-learning. A French questionnaire measuring regulatory focus was designed and validated. Two user studies (brief interaction with artificial agents vs. repeated sessions with animated agents), where the regulatory focus of agents is conveyed via game strategies, enabled the study of regulatory focus perception and its impact on the interaction. Our results support the use of regulatory focus in affective computing and open perspectives on the theoretical and methodological links between computer science and psychology.

Illegitimate acquisition and use of data is a problematic issue faced by many organizations operating web servers on the internet today. Despite frameworks of rules to prevent ”scraping bots” from carrying out this action, they have developed advanced methods to continue taking data. Following research into what the problem is and how it can be handled, this report identifies and evaluates how machine learning can be used to detect bots. Since developing and testing a machine learning solution proved difficult, an alternative solution was also developed aiming to polarize (separate) bot and human traffic through behavioral analysis. This particular solution to optimize traffic session classification is presented and discussed, as well as, other key findings which can help in detecting and preventing these unwanted visitors. / Olaglig insamling och användning av data är problematiskt för många organisationer som idag använder sig av webbservrar på internet. Trots ramar av regler för att förhindra ”scraping bots” så har de utvecklat avancerade sätt att komma åt data. Efter forskning om vad problemet är och hur det kan hanteras, identifierar och evaluerar denna rapport hur maskininlärning kan användas för att detektera bottar. Då utvecklingen och testningen av en lösning med hjälp av maskininlärning visade sig bli svårt, utvecklades en alternativ lösning med målet att polarisera (separera) bottrafik och legitim trafik. Denna lösning presenteras och diskuteras i rapporten tillsammans med andra nyckelresultat som kan hjälpa till att upptäcka och förhindra dessa oönskade besökare.

Магистерская диссертация Оглуздина К.А. "Когнитивная модель агента в качестве интеллектуальной системы, взаимодействующей с внешней средой" посвящена проблеме моделирования когнитивных агентов. В XX веке разрабатывались особого типа машины, которые могли бы осуществлять мыслительную деятельность, сопоставимую с человеческой. Этот проект был назван искусственный интеллект. Исчерпывающие представления об области искусственного интеллекта можно получить из работ С. Рассела, П. Норвинга, Гаазе-Рапопорт М. Г., Поспелова Д. А., Редько В. Г. и других. Объектом исследования являются искусственные интеллектуальные системы, а предметом - искусственные интеллектуальные агенты. Целью исследования является выявление основных свойств искусственных интеллектуальных агентов. Работа состоит из двух частей, каждая из которых поделена на два раздела. В первой части работы, в первом блоке изучается понятие интеллекта и его особенностей относительно естественных и искусственных систем, а второй блок посвящен анализу понятия агент, рассмотрению классификаций искусственных агентов в целом, и когнитивных агентов в частности. Во второй части работы, в первом блоке мы рассматриваем архитектуры когнитивных агентов, а также проведён их сравнительный анализ. Второй блок посвящен проблеме взаимодействия агентов в многоагентной системе. / Master's thesis of Ogluzdin K.A. "The cognitive agent model as an intellectual system interacting with the external environment" is devoted to the modeling of cognitive agents. In XX century, there was developed a special type of machine that could perform mental activity comparable to that of the human. This project was called artificial intelligence. Comprehensive understanding of the field of artificial intelligence can be obtained from the works of S. Russell, P. Norvinga, M.G. Haase-Rapoport, D.A. Pospelov, V.G. Redko and others. The object of study is the artificial intelligence system, and the subject is artificial intelligent agents. The aim of the study is to identify the basic properties of artificial intelligent agents. The paper consists of two parts, each of which is divided into two blocks. In the first part, in the first block there is studied the concept of intelligence and its features with respect to natural and artificial systems, while the second block is devoted to the analysis of the concept of agent, considering the classification of artificial agents in general, and cognitive agents in particular. In the second part, there is studied in the first section the architecture of cognitive agents, and there is conducted comparative analysis. The second block is devoted to the interaction between agents in multi-agent system.