22 research outputs found
SystĂšme intelligent pour le suivi et lâoptimisation de lâĂ©tat cognitif
Les Ă©motions des ĂȘtres humains changent rĂ©guliĂšrement et parfois de maniĂšre brusque entrainant un changement de lâĂ©tat mental câest-Ă -dire de lâaptitude cĂ©rĂ©brale Ă fonctionner normalement. Il en rĂ©sulte une capacitĂ© cognitive (ou Ă©tat cognitif) de lâindividu Ă pouvoir raisonner, accĂ©der Ă la mĂ©moire, ou effectuer des dĂ©ductions, variable selon lâĂ©tat mental. Ceci affecte, en consĂ©quence, les performances des utilisateurs qui varient en fonction de leurs Ă©tat cognitifs. Cette thĂšse vise Ă optimiser lâĂ©tat cognitif dâun utilisateur lors de ses interactions avec un environnement virtuel. Comme cet Ă©tat dĂ©pend des Ă©motions, lâoptimisation de lâĂ©tat cognitif peut ĂȘtre rĂ©alisĂ©e Ă travers lâoptimisation des Ă©motions et en particulier la rĂ©duction des Ă©motions nĂ©gatives.
Une premiĂšre partie concerne les moyens de mesurer en temps rĂ©el (par un Module de mesures) lâĂ©tat Ă©motionnel et mental dâun utilisateur lors de ses interactions avec un environnement virtuel. Nous avons rĂ©alisĂ© pour cela quatre Ă©tudes expĂ©rimentales avec quatre environnements diffĂ©rents. Nous avons montrĂ© que ces mesures peuvent ĂȘtre rĂ©alisĂ©es en utilisant diffĂ©rents capteurs physiologiques. Nous avons aussi montrĂ© quâil est possible de prĂ©dire la tendance de lâexcitation (un Ă©tat mental) Ă partir dâun traceur de regard.
Dans une deuxiĂšme partie, nous prĂ©sentons lâAgent Neural qui modifie les environnements virtuels afin de provoquer une modification de lâĂ©tat Ă©motionnel dâun utilisateur pour amĂ©liorer son Ă©tat cognitif. Nous avons rĂ©alisĂ© quatre Ă©tudes expĂ©rimentales avec quatre environnements virtuels, oĂč lâAgent Neural intervient dans ces environnements afin de changer lâĂ©tat Ă©motionnel de lâutilisateur. Nous avons montrĂ© que lâagent est capable dâintervenir dans plusieurs types dâenvironnements et de modifier les Ă©motions de lâutilisateur.
Dans une troisiĂšme partie, prĂ©sentons lâAgent Limbique, qui personnalise et amĂ©liore les adaptations faites par lâAgent Neural Ă travers lâobservation et lâapprentissage des impacts des changements des environnements virtuels et des rĂ©actions Ă©motionnelles des utilisateurs. Nous avons montrĂ© que cet agent est capable dâanalyser les interventions de lâAgent Neural et de les modifier. Nous avons montrĂ© aussi que lâAgent Limbique est capable de gĂ©nĂ©rer une nouvelle rĂšgle dâintervention et de prĂ©dire son impact sur lâutilisateur.
La combinaison du Module de mesures, de lâAgent Neural, et de lâAgent Limbique, nous a permis de crĂ©er un systĂšme de contrĂŽle cognitif intelligent que nous avons appelĂ© SystĂšme Limbique Digital.The humanâs emotions change regularly and sometimes suddenly leading to changes in their mental state which is the brainâs ability to function normally. This mental stateâs changes affect the usersâ cognitive ability (or cognitive state) to reason, access memory, or make inferences, which varies depending on the mental state. Consequently, this affects the usersâ performances which varies according to their cognitive states. This thesis aims to optimize the usersâ cognitive state during their interactions with a virtual environment. Since this state depends on emotions, optimization of cognitive state can be achieved through the optimization of emotions and in particular the reduction of negative emotions.
In a first part, we present the means of measuring in real time (using a Measuring module) the usersâ emotional and mental state during their interactions with a virtual environment. We performed four experimental studies with four different environments. We have shown that these measurements can be performed using different physiological sensors. We have also shown that it is possible to predict the tendency of excitement (a mental state) using an eye tracker.
In a second part, we present the Neural Agent which modifies virtual environments to provoke a modification on the usersâ emotional state in order to improve their cognitive state. We performed four experimental studies with four virtual environments, in which the Neural Agent intervenes in these environments to change the usersâ emotional state. We have shown that the agent is able to intervene in several types of environments and able to modify the usersâ emotions.
In a third part, we present the Limbic Agent, which personalizes and improves the adaptations performed by the Neural Agent through the observation and the learning from the virtual environments changesâ impacts and the usersâ emotional reactions. We have shown that this agent is able to analyze the Neural Agentâs interventions and able to modify them. We have also shown that the Limbic Agent is able to generate a new intervention rule and predict its impact on the user.
The combination of the Measuring Module, the Neural Agent, and the Limbic Agent, allowed us to create an intelligent cognitive control system that we called the Digital Limbic System
MOON: Assisting Students in Completing Educational Notebook Scenarios
Jupyter notebooks are increasingly being adopted by teachers to deliver
interactive practical sessions to their students. Notebooks come with many
attractive features, such as the ability to combine textual explanations,
multimedia content, and executable code alongside a flexible execution model
which encourages experimentation and exploration. However, this execution model
can quickly become an issue when students do not follow the intended execution
order of the teacher, leading to errors or misleading results that hinder their
learning. To counter this adverse effect, teachers usually write detailed
instructions about how students are expected to use the notebooks. Yet, the use
of digital media is known to decrease reading efficiency and compliance with
written instructions, resulting in frequent notebook misuse and students
getting lost during practical sessions. In this article, we present a novel
approach, MOON, designed to remedy this problem. The central idea is to provide
teachers with a language that enables them to formalize the expected usage of
their notebooks in the form of a script and to interpret this script to guide
students with visual indications in real time while they interact with the
notebooks. We evaluate our approach using a randomized controlled experiment
involving 21 students, which shows that MOON helps students comply better with
the intended scenario without hindering their ability to progress. Our
follow-up user study shows that about 75% of the surveyed students perceived
MOON as rather useful or very useful
Causal Discovery from Temporal Data: An Overview and New Perspectives
Temporal data, representing chronological observations of complex systems,
has always been a typical data structure that can be widely generated by many
domains, such as industry, medicine and finance. Analyzing this type of data is
extremely valuable for various applications. Thus, different temporal data
analysis tasks, eg, classification, clustering and prediction, have been
proposed in the past decades. Among them, causal discovery, learning the causal
relations from temporal data, is considered an interesting yet critical task
and has attracted much research attention. Existing casual discovery works can
be divided into two highly correlated categories according to whether the
temporal data is calibrated, ie, multivariate time series casual discovery, and
event sequence casual discovery. However, most previous surveys are only
focused on the time series casual discovery and ignore the second category. In
this paper, we specify the correlation between the two categories and provide a
systematical overview of existing solutions. Furthermore, we provide public
datasets, evaluation metrics and new perspectives for temporal data casual
discovery.Comment: 52 pages, 6 figure
Bittm: A core biterms-based topic model for targeted analysis
While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency
Goal reasoning for autonomous agents using automated planning
MenciĂłn Internacional en el tĂtulo de doctorAutomated planning deals with the task of finding a sequence of actions, namely
a plan, which achieves a goal from a given initial state. Most planning research
consider goals are provided by a external user, and agents just have to find a
plan to achieve them. However, there exist many real world domains where
agents should not only reason about their actions but also about their goals,
generating new ones or changing them according to the perceived environment.
In this thesis we aim at broadening the goal reasoning capabilities of planningbased
agents, both when acting in isolation and when operating in the same
environment as other agents.
In single-agent settings, we firstly explore a special type of planning tasks
where we aim at discovering states that fulfill certain cost-based requirements
with respect to a given set of goals. By computing these states, agents are able
to solve interesting tasks such as find escape plans that move agents in to safe
places, hide their true goal to a potential observer, or anticipate dynamically arriving
goals. We also show how learning the environmentâs dynamics may help
agents to solve some of these tasks. Experimental results show that these states
can be quickly found in practice, making agents able to solve new planning
tasks and helping them in solving some existing ones.
In multi-agent settings, we study the automated generation of goals based on
other agentsâ behavior. We focus on competitive scenarios, where we are interested
in computing counterplans that prevent opponents from achieving their
goals. We frame these tasks as counterplanning, providing theoretical properties
of the counterplans that solve them. We also show how agents can benefit
from computing some of the states we propose in the single-agent setting to
anticipate their opponentâs movements, thus increasing the odds of blocking
them. Experimental results show how counterplans can be found in different
environments ranging from competitive planning domains to real-time strategy
games.Programa de Doctorado en Ciencia y TecnologĂa InformĂĄtica por la Universidad Carlos III de MadridPresidenta: Eva OnaindĂa de la Rivaherrera.- Secretario: Ăngel GarcĂa Olaya.- Vocal: Mark Robert
Goal Reasoning: Papers from the ACS Workshop
This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning,
which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta,
Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of
which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated
Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal
Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013
Semantic relations between sentences: from lexical to linguistically inspired semantic features and beyond
This thesis is concerned with the identification of semantic equivalence between pairs of natural language
sentences, by studying and computing models to address Natural Language Processing tasks where some
form of semantic equivalence is assessed. In such tasks, given two sentences, our models output either
a class label, corresponding to the semantic relation between the sentences, based on a predefined set
of semantic relations, or a continuous score, corresponding to their similarity on a predefined scale. The
former setup corresponds to the tasks of Paraphrase Identification and Natural Language Inference, while
the latter corresponds to the task of Semantic Textual Similarity.
We present several models for English and Portuguese, where various types of features are considered,
for instance based on distances between alternative representations of each sentence, following lexical
and semantic frameworks, or embeddings from pre-trained Bidirectional Encoder Representations from
Transformers models. For English, a new set of semantic features is proposed, from the formal semantic
representation of Discourse Representation Structure. In Portuguese, suitable corpora are scarce and formal
semantic representations are unavailable, hence an evaluation of currently available features and corpora is
conducted, following the modelling setup employed for English.
Competitive results are achieved on all tasks, for both English and Portuguese, particularly when considering
that our models are based on generally available tools and technologies, and that all features and models are
suitable for computation in most modern computers, except for those based on embeddings. In particular,
for English, our semantic features from DRS are able to improve the performance of other models, when
integrated in the feature set of such models, and state of the art results are achieved for Portuguese, with
models based on fine tuning embeddings to a specific task; SumĂĄrio:
RelaçÔes semùnticas entre frases: de aspectos
lexicais a aspectos semĂąnticos inspirados em
linguĂstica e alĂ©m destes
Esta tese Ă© dedicada Ă identificação de equivalĂȘncia semĂąntica entre frases em lĂngua natural, atravĂ©s do
estudo e computação de modelos destinados a tarefas de Processamento de Linguagem Natural relacionadas
com alguma forma de equivalĂȘncia semĂąntica. Em tais tarefas, a partir de duas frases, os nossos modelos
produzem uma etiqueta de classificação, que corresponde à relação semùntica entre as frases, baseada
num conjunto predefinido de possĂveis relaçÔes semĂąnticas, ou um valor contĂnuo, que corresponde Ă
similaridade das frases numa escala predefinida. A primeira configuração mencionada corresponde às tarefas
de Identificação de ParĂĄfrases e de InferĂȘncia em LĂngua Natural, enquanto que a Ășltima configuração
mencionada corresponde Ă tarefa de Similaridade SemĂąntica em Texto.
Apresentamos diversos modelos para InglĂȘs e PortuguĂȘs, onde vĂĄrios tipos de aspectos sĂŁo considerados,
por exemplo baseados em distùncias entre representaçÔes alternativas para cada frase, seguindo formalismos
semùnticos e lexicais, ou vectores contextuais de modelos previamente treinados com RepresentaçÔes
Codificadas Bidirecionalmente a partir de Transformadores. Para InglĂȘs, propomos um novo conjunto de
aspectos semùnticos, a partir da representação formal de semùntica em Estruturas de Representação de
Discurso. Para PortuguĂȘs, os conjuntos de dados apropriados sĂŁo escassos e nĂŁo estĂŁo disponĂveis representaçÔes
formais de semĂąntica, entĂŁo implementĂĄmos uma avaliação de aspectos actualmente disponĂveis,
seguindo a configuração de modelos aplicada para InglĂȘs.
Obtivemos resultados competitivos em todas as tarefas, em InglĂȘs e PortuguĂȘs, particularmente considerando
que os nossos modelos sĂŁo baseados em ferramentas e tecnologias disponĂveis, e que todos
os nossos aspectos e modelos são apropriados para computação na maioria dos computadores modernos,
excepto os modelos baseados em vectores contextuais. Em particular, para InglĂȘs, os nossos aspectos
semùnticos a partir de Estruturas de Representação de Discurso melhoram o desempenho de outros modelos,
quando integrados no conjunto de aspectos de tais modelos, e obtivemos resultados estado da arte
para PortuguĂȘs, com modelos baseados em afinação de vectores contextuais para certa tarefa