Embedding of the Personalized Sentiment Engine PERSEUS in an Artificial Companion

The term Artificial Companion has originally been introduced by Y. Wilks [1] as “...an intelligent and helpful cognitive agent, which appears to know its owner and their habits, chats to them and diverts them, assists them with simple tasks. . . ”. To serve the users’ interests by considering a personal knowledge is, furthermore, demanded. The following position paper takes this request as motivation for the embedding of the PERSEUS system, which is a personalized sentiment framework based on a Deep Learning approach. We discuss how such an embedding with a group of users should be realized and why the utilization of PERSEUS is beneficial

COMBINED ARTIFICIAL INTELLIGENCE BEHAVIOUR SYSTEMS IN SERIOUS GAMING

This thesis proposes a novel methodology for creating Artificial Agents with semi-realistic behaviour, with such behaviour defined as overcoming common limitations of mainstream behaviour systems; rapidly switching between actions, ignoring “obvious” event priorities, etc. Behaviour in these Agents is not fully realistic as some limitations remain; Agents have a “perfect” knowledge about the surrounding environment, and an inability to transfer knowledge to other Agents (no communication). The novel methodology is achieved by hybridising existing Artificial Intelligence (AI) behaviour systems. In most artificial agents (Agents) behaviour is created using a single behaviour system, whereas this work combines several systems in a novel way to overcome the limitations of each. A further proposal is the separation of behavioural concerns into behaviour systems that are best suited to their needs, as well as describing a biologically inspired memory system that further aids in the production of semi-realistic behaviour. Current behaviour systems are often inherently limited, and in this work it is shown that by combining systems that are complementary to each other, these limitations can be overcome without the need for a workaround. This work examines in detail Belief Desire Intention systems, as well as Finite State Machines and explores how these methodologies can complement each other when combined appropriately. By combining these systems together a hybrid system is proposed that is both fast to react and simple to maintain by separating behaviours into fast-reaction (instinctual) and slow-reaction (behavioural) behaviours, and assigning these to the most appropriate system. Computational intelligence learning techniques such as Artificial Neural Networks have been intentionally avoided, as these techniques commonly present their data in a “black box” system, whereas this work aims to make knowledge explicitly available to the user. A biologically inspired memory system has further been proposed in order to generate additional behaviours in Artificial Agents, such as behaviour related to forgetfulness. This work explores how humans can quickly recall information while still being able to store millions of pieces of information, and how this can be achieved in an artificial system

Reconnaissance automatique des dimensions affectives dans l'interaction orale homme-machine pour des personnes dépendantes

La majorité des systèmes de reconnaissance d'états affectifs est entrainée sur des données artificielles hors contexte applicatif et les évaluations sont effectuées sur des données pré-enregistrées de même qualité. Cette thèse porte sur les différents défis résultant de la confrontation de ces systèmes à des situations et des utilisateurs réels.Pour disposer de données émotionnelles spontanées au plus proche de la réalité, un système de collecte simulant une interaction naturelle et mettant en oeuvre un agent virtuel expressif a été développé. Il a été mis en oeuvre pour recueillir deux corpus émotionnels, avec la participation de près de 80 patients de centres médicaux de la région de Montpellier, dans le cadre du projet ANR ARMEN.Ces données ont été utilisées dans l'exploration d'approches pour la résolution du problème de la généralisation des performances des systèmes de détection des émotions à d'autres données. Dans cette optique, une grande partie des travaux menés a porté sur des stratégies cross-corpus ainsi que la sélection automatique des meilleurs paramètres. Un algorithme hybride combinant des techniques de sélection flottante avec des métriques de similitudes et des heuristiques multi-échelles a été proposé et appliqué notamment dans le cadre d'un challenge (InterSpeech 2012). Les résultats de l'application de cet algorithme offrent des pistes pour différencier des corpus émotionnels à partir des paramètres les plus pertinents pour les représenter.Un prototype du système de dialogue complet, incluant le module de détection des émotions et l'agent virtuel a également été implémenté.Most of the affective states recognition systems are trained on artificial data, without any realistic context. Moreover the evaluations are done with pre-recorded data of the same quality. This thesis seeks to tackle the various challenges resulting from the confrontation of these systems with real situations and users.In order to obtain close-to-reality spontaneous emotional data, a data-collection system simulating a natural interaction was developed. It uses an expressive virtual character to conduct the interaction. Two emotional corpora where gathered with this system, with almost 80 patients from medical centers of the region of Montpellier, France, participating in. This work was carried out as part of the French ANR ARMEN collaborative project.This data was used to explore approaches to solve the problem of performance generalization for emotion detection systems. Most of the work in this part deals with cross-corpus strategies and automatic selection of the best features. An hybrid algorithm combining floating selection techniques with similarity measures and multi-scale heuristics was proposed and used in the frame of the InterSpeech 2012 Emotino Challenge. The results and insights gained with the help of this algorithm suggest ways of distinguishing between emotional corpora using their most relevant features.A prototype of the complete dialog system, including the emotion detection module and the virtual agent was also implemented.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Ich, Du, Wir - Ein Personengedächtnis für einen künstlichen Gesprächspartner

Mattar N. Ich, Du, Wir - Ein Personengedächtnis für einen künstlichen Gesprächspartner. Bielefeld: Bielefeld University; 2014

Learning Internal State Memory Representations from Observation

Learning from Observation (LfO) is a machine learning paradigm that mimics how people learn in daily life: learning how to do something simply by watching someone else do it. LfO has been used in various applications, from video game agent creation to driving a car, but it has always been limited by the inability of an observer to know what a performing entity chooses to remember as they act in an environment. Various methods have either ignored the effects of memory or otherwise made simplistic assumptions about its structure. In this dissertation, we propose a new method, Memory Composition Learning, that captures the influence of a performer\u27s memory in an observed behavior through the creation of an auxiliary memory feature set that explicitly models the aspects of the environment with significance for future decisions, and which can be used with a machine learning technique to provide salient information from memory. It advances the state of the art by automatically learning the internal structure of memory instead of ignoring or predefining it. This research is difficult in that memory modeling is an unsupervised learning problem that we elect to solve solely from unobtrusive observation. This research is significant for LfO in that it will allow learning techniques that otherwise could not use information from memory to use a tailored set of learned memory features that capture salient influences from memory and enable decision-making based on these influences for more effective learning performance. To validate our hypothesis, we implemented a prototype for modeling observed memory influences with our approach and applied it to simulated vacuum cleaner and lawn mower domains. Our investigation revealed that MCL was able to automatically learn memory features that describe the influences on an observed actor\u27s internal state, and which improved learning performance of observed behaviors