10,053 research outputs found

    The use of emotions in the implementation of various types of learning in a cognitive agent

    Get PDF
    Les tuteurs professionnels humains sont capables de prendre en considĂ©ration des Ă©vĂ©nements du passĂ© et du prĂ©sent et ont une capacitĂ© d'adaptation en fonction d'Ă©vĂ©nements sociaux. Afin d'ĂȘtre considĂ©rĂ© comme une technologie valable pour l'amĂ©lioration de l'apprentissage humain, un agent cognitif artificiel devrait pouvoir faire de mĂȘme. Puisque les environnements dynamiques sont en constante Ă©volution, un agent cognitif doit pareillement Ă©voluer et s'adapter aux modifications structurales et aux phĂ©nomĂšnes nouveaux. Par consĂ©quent, l'agent cognitif idĂ©al devrait possĂ©der des capacitĂ©s d'apprentissage similaires Ă  celles que l'on retrouve chez l'ĂȘtre humain ; l'apprentissage Ă©motif, l'apprentissage Ă©pisodique, l'apprentissage procĂ©dural, et l'apprentissage causal. Cette thĂšse contribue Ă  l'amĂ©lioration des architectures d'agents cognitifs. Elle propose 1) une mĂ©thode d'intĂ©gration des Ă©motions inspirĂ©e du fonctionnement du cerveau; et 2) un ensemble de mĂ©thodes d'apprentissage (Ă©pisodique, causale, etc.) qui tiennent compte de la dimension Ă©motionnelle. Le modĂšle proposĂ© que nous avons appelĂ© CELTS (Conscious Emotional Learning Tutoring System) est une extension d'un agent cognitif conscient dans le rĂŽle d'un tutoriel intelligent. Il comporte un module de gestion des Ă©motions qui permet d'attribuer des valences Ă©motionnelles positives ou nĂ©gatives Ă  chaque Ă©vĂ©nement perçu par l'agent. Deux voies de traitement sont prĂ©vues : 1) une voie courte qui permet au systĂšme de rĂ©pondre immĂ©diatement Ă  certains Ă©vĂ©nements sans un traitement approfondis, et 2) une voie longue qui intervient lors de tout Ă©vĂ©nement qui exige la volition. Dans cette perspective, la dimension Ă©motionnelle est considĂ©rĂ©e dans les processus cognitifs de l'agent pour la prise de dĂ©cision et l'apprentissage. L'apprentissage Ă©pisodique dans CELTS est basĂ© sur la thĂ©orie du Multiple Trace Memory consolidation qui postule que lorsque l'on perçoit un Ă©vĂ©nement, l'hippocampe fait une premiĂšre interprĂ©tation et un premier apprentissage. Ensuite, l'information acquise est distribuĂ©e aux diffĂ©rents cortex. Selon cette thĂ©orie, la reconsolidation de la mĂ©moire dĂ©pend toujours de l'hippocampe. Pour simuler de tel processus, nous avons utilisĂ© des techniques de fouille de donnĂ©es qui permettent la recherche de motifs sĂ©quentiels frĂ©quents dans les donnĂ©es gĂ©nĂ©rĂ©es durant chaque cycle cognitif. L'apprentissage causal dans CELTS se produit Ă  l'aide de la mĂ©moire Ă©pisodique. Il permet de trouver les causes et les effets possibles entre diffĂ©rents Ă©vĂ©nements. Il est mise en Ɠuvre grĂące Ă  des algorithmes de recherche de rĂšgles d'associations. Les associations Ă©tablies sont utilisĂ©es pour piloter les interventions tutorielles de CELTS et, par le biais des rĂ©ponses de l'apprenant, pour Ă©valuer les rĂšgles causales dĂ©couvertes. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : agents cognitifs, Ă©motions, apprentissage Ă©pisodique, apprentissage causal

    Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

    Get PDF
    Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

    Projective simulation for artificial intelligence

    Get PDF
    We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine

    Life is an Adventure! An agent-based reconciliation of narrative and scientific worldviews\ud

    Get PDF
    The scientific worldview is based on laws, which are supposed to be certain, objective, and independent of time and context. The narrative worldview found in literature, myth and religion, is based on stories, which relate the events experienced by a subject in a particular context with an uncertain outcome. This paper argues that the concept of “agent”, supported by the theories of evolution, cybernetics and complex adaptive systems, allows us to reconcile scientific and narrative perspectives. An agent follows a course of action through its environment with the aim of maximizing its fitness. Navigation along that course combines the strategies of regulation, exploitation and exploration, but needs to cope with often-unforeseen diversions. These can be positive (affordances, opportunities), negative (disturbances, dangers) or neutral (surprises). The resulting sequence of encounters and actions can be conceptualized as an adventure. Thus, the agent appears to play the role of the hero in a tale of challenge and mystery that is very similar to the "monomyth", the basic storyline that underlies all myths and fairy tales according to Campbell [1949]. This narrative dynamics is driven forward in particular by the alternation between prospect (the ability to foresee diversions) and mystery (the possibility of achieving an as yet absent prospect), two aspects of the environment that are particularly attractive to agents. This dynamics generalizes the scientific notion of a deterministic trajectory by introducing a variable “horizon of knowability”: the agent is never fully certain of its further course, but can anticipate depending on its degree of prospect

    Méthodes d'apprentissage inspirées de l'humain pour un tuteur cognitif artificiel

    Get PDF
    Les systĂšmes tuteurs intelligents sont considĂ©rĂ©s comme un remarquable concentrĂ© de technologies qui permettent un processus d'apprentissage. Ces systĂšmes sont capables de jouer le rĂŽle d'assistants voire mĂȘme de tuteur humain. Afin d'y arriver, ces systĂšmes ont besoin de maintenir et d'utiliser une reprĂ©sentation interne de l'environnement. Ainsi, ils peuvent tenir compte des Ă©vĂšnements passĂ©s et prĂ©sents ainsi que de certains aspects socioculturels. ParallĂšlement Ă  l'Ă©volution dynamique de l'environnement, un agent STI doit Ă©voluer en modifiant ses structures et en ajoutant de nouveaux phĂ©nomĂšnes. Cette importante capacitĂ© d'adaptation est observĂ©e dans le cas de tuteurs humains. Les humains sont capables de gĂ©rer toutes ces complexitĂ©s Ă  l'aide de l'attention et du mĂ©canisme de conscience (Baars B. J., 1983, 1988), et (Sloman, A and Chrisley, R., 2003). Toutefois, reconstruire et implĂ©menter des capacitĂ©s humaines dans un agent artificiel est loin des possibilitĂ©s actuelles de la connaissance de mĂȘme que des machines les plus sophistiquĂ©es. Pour rĂ©aliser un comportement humanoĂŻde dans une machine, ou simplement pour mieux comprendre l'adaptabilitĂ© et la souplesse humaine, nous avons Ă  dĂ©velopper un mĂ©canisme d'apprentissage proche de celui de l'homme. Ce prĂ©sent travail dĂ©crit quelques concepts d'apprentissage fondamentaux implĂ©mentĂ©s dans un agent cognitif autonome, nommĂ© CTS (Conscious Tutoring System) dĂ©veloppĂ© dans le GDAC (Dubois, D., 2007). Nous proposons un modĂšle qui Ă©tend un apprentissage conscient et inconscient afin d'accroĂźtre l'autonomie de l'agent dans un environnement changeant ainsi que d'amĂ©liorer sa finesse. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Apprentissage, Conscience, Agent cognitif, Codelet

    Creating and Capturing Artificial Emotions in Autonomous Robots and Software Agents

    Get PDF
    This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS is able to create and capture artificial emotions during interactions with its environment, and we describe the underlying mechanisms for this. The control system also realizes the capturing of knowledge about its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, represents these artificial emotions. For this, we devise a formalism which enriches the traditional factual knowledge in knowledge graphs with the representation of artificial emotions. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes the user’s orders. The environment of this user assistant consists of autonomous service agents. The execution of user’s orders requires interaction with these autonomous service agents. These interactions lead to artificial emotions within the assistant. The first experiments show that it is possible to realize an autonomous agent with plausible artificial emotions with ARTEMIS and to record these artificial emotions in its Agent Knowledge Graph. In this way, autonomous agents based on ARTEMIS can capture essential knowledge that supports successful planning and decision making in complex dynamic environments and surpass emotionless agents

    A Comparative Study of Cognitive Systems for Learning

    Get PDF
    Learning is the modification of a behavioral tendency by experience. Memory and reasoning are the most important aspects for learning in humans; information is temporarily stored in the short-term memory and processed, compared with existing memories and stored in long-term memory, and can be re-used when needed. One way to describe an organized pattern of thought or behavior and the categories of information along with their relationships is by using schemas. A cognitive script is one form of a schema that evolves over multiple exposures to the same set of stimuli and/or repeated enactment of a particular behavior. This research aims to provide a comparative study between three cognitive systems/tools designed to allow learning, by using cognitive scripts representation. Since retrieving and re-using past experiences is the core of any learning process, the focus of this thesis is to examine the current existing cognitive systems and tools to evaluate their ability to retrieve past experiences. SOAR, myCBR and Pharaoh are three systems considered for this thesis. Linear and multi-branched cognitive scripts were considered in order to measure the capacity of those systems to allow learning using cognitive scripts representation. The results of this work show that SOAR, myCBR and Pharaoh took almost the same time to retrieve a set of similar cognitive scripts to a query script. However, SOAR was able to retrieve one similar script only, while myCBR and Pharaoh were able to retrieve multiple scripts. Pharaoh tops the other two system in its ability to handle multibranched scripts of different sizes and the way it considers context

    What working memory is for

    Get PDF
    • 

    corecore