Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECA's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.

Work email is one of the organisations most critical tool today. It`s have become a standard way to communicate internally and externally. It can also affect our well-being. Email overload has become a well-known issue for many people. With interviews, follow up interviews, and a workshop, three persons from an accounting firm prioritise pre-define emails needs. And identified several other email needs that were added to the priority list. A thematic analysis and summarizing of a Likert scale was conducted to identify underlying work email needs and work email needs that are not apparent. Three work email needs were selected and using scenario-based methods and the elements of PACT to investigating how the characteristics of a chatbot can help solve the identified work email overload issue? The result shows that email overload is percept different from individual to individual. The choice of how email is handled and email activities indicate how email overload feeling is experienced. The result shows a need to get a sense of the email content quickly, fast collect financial information and information from Swedish authorities, and repetitive, time-consuming tasks. Suggestions on how this problem can be solved have been put forward for many years, and how to use machine learning to help reduce email overload. However, many of these proposed solutions have not yet been implemented on a full scale. One conclusion may be that since email overload is not experienced in the same way, individuals have different needs - One solution does not fit all. With the help of the character of a chatbot, many problems can be solved. And with a technological character of a chatbot that can learn individuals' email patterns, suggest email task to the user and performing tasks to reducing the email overload perception. Using keyword for email intents to get a sense of the email content faster and produce quick links where to find information about the identified subject. And to work preventive give the user remainder and perform repetitive tasks on specific dates.

Dans une interaction humain-agent, l’engagement de l’utilisateur est un élément essentiel pour atteindre l’objectif de l’interaction. Dans cette thèse, nous étudions comment l’engagement de l’utilisateur pourrait être favorisé par le comportement de l’agent. Nous nous concentrons sur les stratégies de comportement verbal de l’agent qui concernent respectivement la forme, le timing et le contenu de ses énoncés. Nous présentons des études empiriques qui concernent certains aspects du comportement de politesse de l’agent, du comportement d’interruption de l’agent, et les sujets de conversation que l’agent adresse lors de l’interaction. Basé sur les résultats de la dernière étude, nous proposons un Gestionnaire de Sujets axé sur l’engagement (modèle computationnel) qui personnalise les sujets d’une interaction dans des conversations où l’agent donne des informations à un utilisateur humain. Le Modèle de Sélection des Sujets du Gestionnaire de Sujets décide sur quoi l’agent devrait parler et quand. Pour cela, il prend en compte la perception par l’agent de l’utilisateur, qui est dynamiquement mis à jour, ainsi que l’état mental et les préférences de l’agent. Le Modèle de Transition de Sujets du Gestionnaire de Sujet, basé sur une étude empirique, calcule comment l’agent doit présenter les sujets dans l’interaction en cours sans perdre la cohérence de l’interaction. Nous avons implémenté et évalué le Gestionnaire de Sujets dans un agent virtuel conversationnel qui joue le rôle d’un visiteur dans un musée. / In human-agent interaction the engagement of the user is an essential aspect to complete the goal of the interaction. In this thesis we study how the user’s engagement could be favoured by the agent’s behaviour. We thereby focus on the agent’s verbal behaviour considering strategies that regard respectively the form, timing, and content of utterances : We present empirical studies that regard (aspects of) the agent’s politeness behaviour, interruption behaviour, and the topics that the agent addresses in the interaction. Based on the outcomes of the latter study we propose an engagement-driven Topic Manager (computational model) that personalises the topics of an interaction in human-agent information-giving chat. The Topic Selection component of the Topic Manager decides what the agent should talk about and when. For this it takes into account the agent’s dynamically updated perception of the user as well as the agent’s own mental state. The Topic Transition component of the Topic Manager, based upon an empirical study, computes how the agent should introduce the topics in the ongoing interaction without loosing the coherence of the interaction. We implemented and evaluated the Topic Manager in a conversational virtual agent that plays the role of a visitor in amuseum.

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