Literatura académica sobre el tema "Apprentissage de modèles d'actions"
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Artículos de revistas sobre el tema "Apprentissage de modèles d'actions":
Hatchuel, Armand y Jean-Claude Moisdon. "Modèles et apprentissage organisationnel". Cahiers d'Economie et sociologie rurales 28, n.º 1 (1993): 17–32. http://dx.doi.org/10.3406/reae.1993.1360.
Bigand, Emmanuel y Charles Delbé. "Apprentissage implicite en musique : Théorie et Modèles". Intellectica. Revue de l'Association pour la Recherche Cognitive 48, n.º 1 (2008): 13–26. http://dx.doi.org/10.3406/intel.2008.1237.
Angéloz, Aline. "APPRENTISSAGE ET MÉMOIRE : LES INSÉPARABLES DANS LE CERVEAU". Cortica 2, n.º 1 (20 de marzo de 2023): 165–69. http://dx.doi.org/10.26034/cortica.2023.3661.
Indjehagopian, Jean-Pierre y Sandrine Macé. "Mesures d'impact de promotion des ventes : Description et comparaison de trois méthodes". Recherche et Applications en Marketing (French Edition) 9, n.º 4 (diciembre de 1994): 53–79. http://dx.doi.org/10.1177/076737019400900403.
Dechemi, N., T. Benkaci y A. Issolah. "Modélisation des débits mensuels par les modèles conceptuels et les systèmes neuro-flous". Revue des sciences de l'eau 16, n.º 4 (12 de abril de 2005): 407–24. http://dx.doi.org/10.7202/705515ar.
Guitard, Paulette. "L'apprentissage expérientiel et l'ergothérapie: Compatibilité théorique et pratique". Canadian Journal of Occupational Therapy 63, n.º 4 (octubre de 1996): 252–59. http://dx.doi.org/10.1177/000841749606300406.
Tremblay, Manon y Jacques Chevrier. "L'apprentissage expérientiel: Un modèle éducotif à Intégrer au processus ergothéraplque". Canadian Journal of Occupational Therapy 60, n.º 5 (diciembre de 1993): 262–70. http://dx.doi.org/10.1177/000841749306000507.
Germain, Marc y Alphonse Magnus. "Anticipations rationnelles et apprentissage. De la pertinence des modèles cohérents". Recherches économiques de Louvain 60, n.º 4 (diciembre de 1994): 481–86. http://dx.doi.org/10.1017/s0770451800004620.
Casanova-Robin, Hélène. "La rhétorique de la légitimité". Rhetorica 32, n.º 4 (2014): 348–61. http://dx.doi.org/10.1525/rh.2014.32.4.348.
Mongin, Philippe. "Les anticipations rationnelles et la rationalité: examen de quelques modèles d'apprentissage". Recherches économiques de Louvain 57, n.º 4 (1991): 319–47. http://dx.doi.org/10.1017/s0770451800010563.
Tesis sobre el tema "Apprentissage de modèles d'actions":
Lesner, Boris. "Planification et apprentissage par renforcement avec modèles d'actions compacts". Caen, 2011. http://www.theses.fr/2011CAEN2074.
We study Markovian Decision Processes represented with Probabilistic STRIPS action models. A first part of our work is about solving those processes in a compact way. To that end we propose two algorithms. A first one based on propositional formula manipulation allows to obtain approximate solutions in tractable propositional fragments such as Horn and 2-CNF. The second algorithm solves exactly and efficiently problems represented in PPDDL using a new notion of extended value functions. The second part is about learning such action models. We propose different approaches to solve the problem of ambiguous observations occurring while learning. Firstly, a heuristic method based on Linear Programming gives good results in practice yet without theoretical guarantees. We next describe a learning algorithm in the ``Know What It Knows'' framework. This approach gives strong theoretical guarantees on the quality of the learned models as well on the sample complexity. These two approaches are then put into a Reinforcement Learning setting to allow an empirical evaluation of their respective performances
Rodrigues, Christophe. "Apprentissage incrémental des modèles d'action relationnels". Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_rodrigues.pdf.
In this thesis, we study machine learning for action. Our work both covers reinforcement learning (RL) and inductive logic programming (ILP). We focus on learning action models. An action model describes the preconditions and effects of possible actions in an environment. It enables anticipating the consequences of the agent’s actions and may also be used by a planner. We specifically work on a relational representation of environments. They allow to describe states and actions by the means of objects and relations between the various objects that compose them. We present the IRALe method, which learns incrementally relational action models. First, we presume that states are fully observable and the consequences of actions are deterministic. We provide a proof of convergence for this method. Then, we develop an active exploration approach which allows focusing the agent’s experience on actions that are supposedly non-covered by the model. Finally, we generalize the approach by introducing a noisy perception of the environment in order to make our learning framework more realistic. We empirically illustrate each approach’s importance on various planification problems. The results obtained show that the number of interactions necessary with the environments is very weak compared to the size of the considered states spaces. Moreover, active learning allows to improve significantly these results
Gaidon, Adrien. "Modèles structurés pour la reconnaissance d'actions dans des vidéos réalistes". Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00780679.
Grand, Maxence. "Apprentissage de Modèle d'Actions basé sur l'Induction Grammaticale Régulière pour la Planification en Intelligence Artificielle". Electronic Thesis or Diss., Université Grenoble Alpes, 2022. http://www.theses.fr/2022GRALM044.
The field of artificial intelligence aims to design and build autonomous agents able to perceive, learn and act without any human intervention to perform complex tasks. To perform complex tasks, the autonomous agent must plan the best possible actions and execute them. To do this, the autonomous agent needs an action model. An action model is a semantic representation of the actions it can execute. In an action model, an action is represented using (1) a precondition: the set of conditions that must be satisfied for the action to be executed and (2) the effects: the set of properties of the world that will be altered by the execution of the action. STRIPS planning is a classical method to design these action models. However, STRIPS action models are generally too restrictive to be used in real-world applications. There are other forms of action models: temporal action models allowing to represent actions that can be executed concurrently, HTN action models allowing to represent actions as tasks and subtasks, etc. These models are less restrictive, but the less restrictive the models are the more difficult they are to design. In this thesis, we are interested in approaches facilitating the acquisition of these action models based on machine learning techniques.In this thesis, we present AMLSI (Action Model Learning with State machine Interaction), an approach for action model learning based on Regular Grammatical Induction. First, we show that the AMLSI approach allows to learn (STRIPS) action models. We will show the different properties of the approach proving its efficiency: robustness, convergence, require few learning data, quality of the learned models. In a second step, we propose two extensions for temporal action model learning and HTN action model learning
Davesne, Frédéric. "Etude de l'émergence de facultés d'apprentissage fiables et prédictibles d'actions réflexes, à partir de modèles paramétriques soumis à des contraintes internes". Phd thesis, Université d'Evry-Val d'Essonne, 2002. http://tel.archives-ouvertes.fr/tel-00375023.
Dans un premier temps, nous donnons des arguments défendant l'idée que les méthodes d'apprentissage classiques ne peuvent pas,
intrinsèquement, répondre à nos exigences de fiabilité et de prédictibilité. Nous pensons que la clé du problème se situe dans la manière dont la communication entre le système apprenant et son environnement est modélisée. Nous illustrons nos propos grâce à un exemple d'apprentissage par renforcement.
Nous présentons une démarche formalisée dans laquelle la communication est une interaction, au sens physique du terme. Le système y est soumis à deux forces: la réaction du système est due à la fois à l'action de l'environnement et au maintient de contraintes internes. L'apprentissage devient
une propriété émergente d'une suite de réactions du système, dans des cas d'interactions favorables. L'ensemble des évolutions possibles du système est déduit par le calcul, en se basant uniquement (sans autre paramètre) sur la connaissance de l'interaction.
Nous appliquons notre démarche à deux sous-systèmes interconnectés, dont l'objectif global est
l'apprentissage d'actions réflexes.
Nous prouvons que le premier possède comme propriété émergente des facultés d'apprentissage par renforcement et d'apprentissage latent fiables et prédictibles.
Le deuxième, qui est ébauché, transforme un signal en une information perceptive. Il fonctionne par sélection d'hypothèses d'évolution du signal au cours du temps à partir d'une mémoire. Des contraintes internes à la mémoire déterminent les ensembles valides d'informations perceptives.
Nous montrons, dans un cas simple, que ces contraintes mènent à un équivalent du théorème de Shannon sur l'échantillonnage.
Dragoni, Laurent. "Tri de potentiels d'action sur des données neurophysiologiques massives : stratégie d’ensemble actif par fenêtre glissante pour l’estimation de modèles convolutionnels en grande dimension". Thesis, Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ4016.
In the nervous system, cells called neurons are specialized in the communication of information. Through the generation and propagation of electrical currents named action potentials, neurons are able to transmit information in the body. Given the importance of the neurons, in order to better understand the functioning of the nervous system, a wide range of methods have been proposed for studying those cells. In this thesis, we focus on the analysis of signals which have been recorded by electrodes, and more specifically, tetrodes and multi-electrode arrays (MEA). Since those devices usually record the activity of a set of neurons, the recorded signals are often a mixture of the activity of several neurons. In order to gain more knowledge from this type of data, a crucial pre-processing step called spike sorting is required to separate the activity of each neuron. Nowadays, the general procedure for spike sorting consists in a three steps procedure: thresholding, feature extraction and clustering. Unfortunately this methodology requires a large number of manual operations. Moreover, it becomes even more difficult when treating massive volumes of data, especially MEA recordings which also tend to feature more neuronal synchronizations. In this thesis, we present a spike sorting strategy allowing the analysis of large volumes of data and which requires few manual operations. This strategy makes use of a convolutional model which aims at breaking down the recorded signals as temporal convolutions between two factors: neuron activations and action potential shapes. The estimation of these two factors is usually treated through alternative optimization. Being the most difficult task, we only focus here on the estimation of the activations, assuming that the action potential shapes are known. Estimating the activations is traditionally referred to convolutional sparse coding. The well-known Lasso estimator features interesting mathematical properties for the resolution of such problem. However its computation remains challenging on high dimensional problems. We propose an algorithm based of the working set strategy in order to compute efficiently the Lasso. This algorithm takes advantage of the particular structure of the problem, derived from biological properties, by using temporal sliding windows, allowing it to scale in high dimension. Furthermore, we adapt theoretical results about the Lasso to show that, under reasonable assumptions, our estimator recovers the support of the true activation vector with high probability. We also propose models for both the spatial distribution and activation times of the neurons which allow us to quantify the size of our problem and deduce the theoretical complexity of our algorithm. In particular, we obtain a quasi-linear complexity with respect to the size of the recorded signal. Finally we present numerical results illustrating both the theoretical results and the performances of our approach
Baccouche, Moez. "Apprentissage neuronal de caractéristiques spatio-temporelles pour la classification automatique de séquences vidéo". Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00932662.
Oneata, Dan. "Modèles robustes et efficaces pour la reconnaissance d'action et leur localisation". Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM019/document.
Video interpretation and understanding is one of the long-term research goals in computer vision. Realistic videos such as movies present a variety of challenging machine learning problems, such as action classification/action retrieval, human tracking, human/object interaction classification, etc. Recently robust visual descriptors for video classification have been developed, and have shown that it is possible to learn visual classifiers in realistic difficult settings. However, in order to deploy visual recognition systems on large-scale in practice it becomes important to address the scalability of the techniques. The main goal is this thesis is to develop scalable methods for video content analysis (eg for ranking, or classification)
Iriart, Alejandro. "Mesures d’insertion sociale destinées aux détenus québécois et récidive criminelle : une approche par l'apprentissage automatique". Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/66717.
In this master thesis, we tried to determine the real influence of social rehabilitation programs on the risk of recidivism. To do this, we used a machine learning algorithm to analyze a database provided by the Quebec Ministry of Public Security (MSP). In this database, we are able to follow the numerous incarcerations of 97,140 prisoners from 2006 to 2018. Our analysis focuses only on inmates who have served in the prison in Quebec City. The approach we used is named Generalized Random Forests (GRF) and was developed by Athey et al. (2019). Our main analysis focuses not only on the characteristics of the prisoners, but also on the results they obtained when they were subjected to the LS/CMI, an extensive questionnaire aimed at determining the criminogenic needs and the risk level of the inmates . We also determined which variables have the most influence on predicting the treatment effect by using a function of the same algorithm that calculates the relative importance of each of the variables to make a prediction. By comparing participants and non-participants, we were able to demonstrate that participating in a program reduces the risk of recidivism by approximately 6.9% for a two-year trial period. Participating in a program always reduces significantly recidivism no matter the definition of recidivism used. We also determined that in terms of personal characteristics, it is the age, the nature of the offence and the number of years of study that are the main predictors for the individual causal effects. As for the LS/CMI, only a few sections of the questionnaire have real predictive power while others, like the one about leisure, do not. In light of our results, we believe that a more efficient instrument capable of predicting recidivism can be created by focusing on the newly identified variables with the greatest predictive power. A better instrument will make it possible to provide better counselling to prisoners on the programs they should follow, and thus increase their chances of being fully rehabilitated.
Arora, Ankuj. "Apprentissage du modèle d'action pour une interaction socio-communicative des hommes-robots". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM081/document.
Driven with the objective of rendering robots as socio-communicative, there has been a heightened interest towards researching techniques to endow robots with social skills and ``commonsense'' to render them acceptable. This social intelligence or ``commonsense'' of the robot is what eventually determines its social acceptability in the long run.Commonsense, however, is not that common. Robots can, thus, only learn to be acceptable with experience. However, teaching a humanoid the subtleties of a social interaction is not evident. Even a standard dialogue exchange integrates the widest possible panel of signs which intervene in the communication and are difficult to codify (synchronization between the expression of the body, the face, the tone of the voice, etc.). In such a scenario, learning the behavioral model of the robot is a promising approach. This learning can be performed with the help of AI techniques. This study tries to solve the problem of learning robot behavioral models in the Automated Planning and Scheduling (APS) paradigm of AI. In the domain of Automated Planning and Scheduling (APS), intelligent agents by virtue require an action model (blueprints of actions whose interleaved executions effectuates transitions of the system state) in order to plan and solve real world problems. During the course of this thesis, we introduce two new learning systems which facilitate the learning of action models, and extend the scope of these new systems to learn robot behavioral models. These techniques can be classified into the categories of non-optimal and optimal. Non-optimal techniques are more classical in the domain, have been worked upon for years, and are symbolic in nature. However, they have their share of quirks, resulting in a less-than-desired learning rate. The optimal techniques are pivoted on the recent advances in deep learning, in particular the Long Short Term Memory (LSTM) family of recurrent neural networks. These techniques are more cutting edge by virtue, and produce higher learning rates as well. This study brings into the limelight these two aforementioned techniques which are tested on AI benchmarks to evaluate their prowess. They are then applied to HRI traces to estimate the quality of the learnt robot behavioral model. This is in the interest of a long term objective to introduce behavioral autonomy in robots, such that they can communicate autonomously with humans without the need of ``wizard'' intervention
Libros sobre el tema "Apprentissage de modèles d'actions":
Mega, Voula P. MODÈLES POUR LES VILLES D'AVENIR - Un kaléidoscope de visions et d'actions pour des villes durables. Paris: Editions L'Harmattan, 2009.
Boucheron, Stéphane. Théorie de l'apprentissage: De l'approche formelle aux enjeux cognitifs. Paris: Hermès, 1992.
Hourst, Bruno. Modèles de jeux de formation: Les jeux-cadres de Thiagi. 3a ed. Paris: Eyrolles-Éd. d'Organisation, 2007.
Kühn, R. Adaptivity and learning: An interdisciplinary debate. Berlin: Springer, 2003.
Vapnik, Vladimir Naumovich. The nature of statistical learning theory. 2a ed. New York: Springer, 2010.
Lo, David. Mining software specifications: Methodologies and applications. Boca Raton, FL: CRC Press, 2011.
Baskin, Ken. Corporate DNA: Learning from life. Boston: Butterworth-Heinemann, 1998.
Rasmussen, Carl Edward. Gaussian processes for machine learning. Cambridge, Mass: MIT Press, 2006.
Rasmussen, Carl Edward. Gaussian processes for machine learning. Cambridge, MA: MIT Press, 2005.
David, Klahr, Langley Pat y Neches Robert, eds. Production system models of learning and development. Cambridge, Mass: MIT Press, 1987.
Capítulos de libros sobre el tema "Apprentissage de modèles d'actions":
Bastien, Claude. "Apprentissage : modèles et représentation". En Intelligence naturelle, intelligence artificielle, 257–68. Presses Universitaires de France, 1993. http://dx.doi.org/10.3917/puf.lenyj.1993.01.0257.
FLEURY SOARES, Gustavo y Induraj PUDHUPATTU RAMAMURTHY. "Comparaison de modèles d’apprentissage automatique et d’apprentissage profond". En Optimisation et apprentissage, 153–71. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch6.
la Tribonnière, X. de y R. Étienne. "Apprentissage, modèles explicatifs et méthodes pédagogiques". En Pratiquer L'éducation Thérapeutique, 69–75. Elsevier, 2023. http://dx.doi.org/10.1016/b978-2-294-77885-8.00009-6.
BELKHARROUBI, Lakhdar y Khadidja YAHYAOUI. "La résolution du problème d’équilibrage d’une chaîne de montage à modèles mixtes". En Optimisation et apprentissage, 93–119. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch4.
Baron, Georges-Louis y Christian Depover. "Chapitre 4 : Effets des technologies numériques sur les modèles pédagogiques et les méthodes d’enseignement-apprentissage". En Les effets du numérique sur l’éducation, 81–92. Presses universitaires du Septentrion, 2019. http://dx.doi.org/10.4000/books.septentrion.138031.