No description available.

Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players, these characters are often allowed to cheat. Although they appear skillful at playing video games, cheating characters may not behave in a human-like manner and can contribute to a lack of player enjoyment if caught. This work investigates the problem of predicting opponent positions in the video game Counter-Strike: Source without cheating. Prediction models are machine-learned from records of past matches and are informed only by game information available to a human player. Results show that the best models estimate opponent positions with similar or better accuracy than human experts. Moreover, the mistakes these models make are closer to human predictions than actual opponent locations perturbed by a corresponding amount of Gaussian noise.

No description available.

Orchards demand large areas of land, thus they are often situated far from major population centres. As a result it is often difficult to obtain the necessary personnel, limiting both growth and productivity. However, if autonomous robots could be integrated into the operation of the orchard, the manpower demand could be reduced. A key problem for any autonomous robot is localisation; how does the robot know where it is? In agriculture robots, the most common approach is to use GPS positioning. However, in an orchard environment, the dense and tall vegetation restricts the usage to large robots that reach above the surroundings. In order to enable the use of smaller robots, it is instead necessary to use a GPS independent system. However, due to the similarity of the environment and the lack of strong recognisable features, it appears unlikely that typical non-GPS solutions will prove successful. Therefore we present a GPS independent localisation system, specifically aimed for orchards, that utilises the inherent structure of the surroundings. Furthermore, we examine and individually evaluate three related sub-problems. The proposed system utilises a 3D point cloud created from a 2D LIDAR and the robot’s movement. First, we show how the data can be segmented into individual trees using a Hidden Semi-Markov Model. Second, we introduce a set of descriptors for describing the geometric characteristics of the individual trees. Third, we present a robust localisation method based on Hidden Markov Models. Finally, we propose a method for detecting segmentation errors when associating new tree measurements with previously measured trees. Evaluation shows that the proposed segmentation method is accurate and yields very few segmentation errors. Furthermore, the introduced descriptors are determined to be consistent and informative enough to allow localisation. Third, we show that the presented localisation method is robust both to noise and segmentation errors. Finally it is shown that a significant majority of all segmentation errors can be detected without falsely labeling correct segmentations as incorrect. / Eftersom fruktodlingar kräver stora markområden är de ofta belägna långt från större befolkningscentra. Detta gör det svårt att finna tillräckligt med arbetskraft och begränsar expansionsmöjligheterna. Genom att integrera autonoma robotar i drivandet av odlingarna skulle arbetet kunna effektiviseras och behovet av arbetskraft minska. Ett nyckelproblem för alla autonoma robotar är lokalisering; hur vet roboten var den är? I jordbruksrobotar är standardlösningen att använda GPS-positionering. Detta är dock problematiskt i fruktodlingar, då den höga och täta vegetationen begränsar användandet till större robotar som når ovanför omgivningen. För att möjliggöra användandet av mindre robotar är det istället nödvändigt att använda ett GPS-oberoende lokaliseringssystem. Detta problematiseras dock av den likartade omgivningen och bristen på distinkta riktpunkter, varför det framstår som osannolikt att existerande standardlösningar kommer fungera i denna omgivning. Därför presenterar vi ett GPS-oberoende lokaliseringssystem, speciellt riktat mot fruktodlingar, som utnyttjar den naturliga strukturen hos omgivningen.Därutöver undersöker vi och utvärderar tre relaterade delproblem. Det föreslagna systemet använder ett 3D-punktmoln skapat av en 2D-LIDAR och robotens rörelse. Först visas hur en dold semi-markovmodell kan användas för att segmentera datasetet i enskilda träd. Därefter introducerar vi ett antal deskriptorer för att beskriva trädens geometriska form. Vi visar därefter hur detta kan kombineras med en dold markovmodell för att skapa ett robust lokaliseringssystem.Slutligen föreslår vi en metod för att detektera segmenteringsfel när nya mätningar av träd associeras med tidigare uppmätta träd. De föreslagna metoderna utvärderas individuellt och visar på goda resultat. Den föreslagna segmenteringsmetoden visas vara noggrann och ge upphov till få segmenteringsfel. Därutöver visas att de introducerade deskriptorerna är tillräckligt konsistenta och informativa för att möjliggöra lokalisering. Ytterligare visas att den presenterade lokaliseringsmetoden är robust både mot brus och segmenteringsfel. Slutligen visas att en signifikant majoritet av alla segmenteringsfel kan detekteras utan att felaktigt beteckna korrekta segmenteringar som inkorrekta.

Multi-state models are often used to evaluate the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. In this dissertation, both multi-state Markov model and semi-Markov model are used to characterize the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient, cognitive states and death as a competing risk. Firstly, a multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data. A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the time to death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Secondly, we further apply a Semi-Markov process in which we assume that the wait- ing times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for the likelihood based estimation. At the end of this dissertation we extend a non-parametric “local EM algorithm” to obtain a smooth estimator of the cause-specific hazard function (CSH) in the presence of competing risk. All the proposed methods are justified by simulation studies and applications to the Nun Study data, a longitudinal study of late life cognition in a cohort of 461 subjects.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23311号 / 情博第747号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 吉井 和佳, 教授 河原 達也, 教授 西野 恒, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Cette thèse consiste à analyser conjointement des signaux de mouvement des yeux et d’électroencéphalogrammes (EEG) multicanaux acquis simultanément avec des participants effectuant une tâche de lecture de recueil d'informations afin de prendre une décision binaire - le texte est-il lié à un sujet ou non? La recherche d'informations textuelles n'est pas un processus homogène dans le temps - ni d'un point de vue cognitif, ni en termes de mouvement des yeux. Au contraire, ce processus implique plusieurs étapes ou phases, telles que la lecture normale, le balayage, la lecture attentive - en termes d'oculométrie - et la création et le rejet d'hypothèses, la confirmation et la décision - en termes cognitifs.Dans une première contribution, nous discutons d'une méthode d'analyse basée sur des chaînes semi-markoviennes cachées sur les signaux de mouvement des yeux afin de mettre en évidence quatre phases interprétables en termes de stratégie d'acquisition d'informations: lecture normale, lecture rapide, lecture attentive et prise de décision.Dans une deuxième contribution, nous lions ces phases aux changements caractéristiques des signaux EEG et des informations textuelles. En utilisant une représentation en ondelettes des EEG, cette analyse révèle des changements de variance et de corrélation des coefficients inter-canaux, en fonction des phases et de la largeur de bande. En utilisant des méthodes de plongement des mots, nous relions l’évolution de la similarité sémantique au sujet tout au long du texte avec les changements de stratégie.Dans une troisième contribution, nous présentons un nouveau modèle dans lequel les EEG sont directement intégrés en tant que variables de sortie afin de réduire l’incertitude des états. Cette nouvelle approche prend également en compte les aspects asynchrones et hétérogènes des données. / This PhD thesis consists in jointly analyzing eye-tracking signals and multi-channel electroencephalograms (EEGs) acquired concomitantly on participants doing an information collection reading task in order to take a binary decision - is the text related to some topic or not ? Textual information search is not a homogeneous process in time - neither on a cognitive point of view, nor in terms of eye-movement. On the contrary, this process involves several steps or phases, such as normal reading, scanning, careful reading - in terms of oculometry - and creation and rejection of hypotheses, confirmation and decision - in cognitive terms.In a first contribution, we discuss an analysis method based on hidden semi-Markov chains on the eye-tracking signals in order to highlight four interpretable phases in terms of information acquisition strategy: normal reading, fast reading, careful reading, and decision making.In a second contribution, we link these phases with characteristic changes of both EEGs signals and textual information. By using a wavelet representation of EEGs, this analysis reveals variance and correlation changes of the inter-channels coefficients, according to the phases and the bandwidth. And by using word embedding methods, we link the evolution of semantic similarity to the topic throughout the text with strategy changes.In a third contribution, we present a new model where EEGs are directly integrated as output variables in order to reduce the state uncertainty. This novel approach also takes into consideration the asynchronous and heterogeneous aspects of the data.

No description available.

De nos jours, la Santé Publique porte de plus en plus d’intérêts aux maladies chroniques neuro-dégénératives liées au vieillissement telles que la démence ou la maladie de Parkinson. Ces pathologies ne peuvent ni être évitées ni guéries et occasionnent une détérioration progressive de la santé des malades requérant donc des soins spécifiques. Le contexte démographique actuel laisse entrevoir un vieillissement de la population et un allongement de l’espérance de vie continus, ce qui aura pour conséquence majeure d’aggraver le fardeau économique, social et démographique de ces maladies dans les années à venir. C’est pourquoi, dans l’optique d’anticiper les problèmes à venir, il est important de développer des modèles statistiques permettant de faire des projections et d’évaluer le fardeau futur de ces maladies via divers indicateurs de santé. De plus, il est intéressant de pouvoir donner des projections,en fonction de scénarios potentiels, qui pourraient être mis en place (e.g. un nouveau traitement), afin d’évaluer l’impact qu’aurait une telle intervention de Santé Publique, mais aussi de prendre en compte une variation de l’incidence due, par exemple, à des changements de comportement. Une approche utilisant le modèle illness-death sous une hypothèse Markovienne a été proposée par Joly et al. 2013 pour répondre à cet objectif. Cette approche est particulièrement adaptée dans ce contexte, car elle permet, notamment, de prendre en compte le risque compétitif qui existe entre devenir malade ou décéder (pour un individu non-malade). L’utilisation de ce modèle pour faire des projections a l’avantage, d’une part, de faire intervenir des projections démographiques nationales pour mieux capter l’évolution de la mortalité au cours du temps, et d’autre part, de proposer une modélisation adaptée de la mortalité (en distinguant la mortalité des malades, des non-malades et générale). C’est sur la base de ce modèle que découle tout le travail de cette thèse. Dans une première partie, les hypothèses du modèle existant ont été améliorées ou modifiées dans le but de considérer l’évolution de l’incidence de la maladie au cours du temps,puis de passer d’un modèle Markovien à un modèle semi-Markovien afin de modéliser la mortalité des malades en fonction de la durée passée avec la maladie. Dans une deuxième partie, la méthode initiale, permettant de considérer l’impact d’une intervention mais avec des hypothèses restrictives, a été développée et généralisée pour prendre en compte des interventions plus flexibles. Puis, les expressions mathématiques/statistiques d’indicateurs de santé pertinents ont été développées dans ce contexte dans le but d’avoir un panel de projections permettant une meilleure évaluation du fardeau futur de la maladie. L’application principale de ce travail a porté sur des projections concernant la démence. Cependant, en appliquant ces modèles à la maladie de Parkinson, nous avons proposé des méthodes permettant d’adapter notre approche à d’autres types de données. / Nowadays, Public Health is more and more interested in the neuro-degenerative chronic diseases related to the ageing such as Dementia or Parkinson’s disease. These pathologies cannot be prevented or cured and cause a progressive deterioration of health, requiring specific cares. The current demographic situation suggests a continuous ageing of the population and a rise of the life expectancy. As consequence of this, the economic, social and demographic burden related to these diseases will worsen in years to come. That is why, developing statistical models, which allow to make projections and to estimate the future burden of chronic diseases via several health indicators, is becoming of paramount importance. Furthermore, it would be interesting to give projections, according to hypothetical scenarios, which could be set up (e.g. a new treatment), to estimate the impact of such Public health intervention, but also to take into account modifications in disease incidence due, for example, to behavior changes. To attend this objective, an approach was proposed by Joly et al. 2013, using the illness-death model under Markovian hypothesis. Such approach has shown to be particularly adapted in this context, since it allows to consider the competive risk existing between the risk of death and the risk to develop the disease (for anon-diseased subject). On one hand, projections made by using this model take advantage of including national demographic projections to better consider the mortality trends over time. Moreover, the approach proposes to carefully model mortality, distinguishing overall mortality, non-diseased and diseased mortality trends. All the work of this thesis have been developed based on this model. In a first part, the hypotheses of the existing model are improved or modified in order to both :consider the evolution of disease incidence over time ; pass from a Markov to a semi-Markov hypothesis, which allows to model the mortality among diseased subjects depending on the time spent with the disease. In a second part, the initial method, allowing to take into account the impact of an intervention, but with many restrictive assumptions, is developed and generalized for more flexible interventions. Then, the mathematical/statistical expressions of relevant health indicators are developed in this context to have a panel of projections giving a better assessment of the future disease burden. The main application of this work concerns projections of Dementia. However, by applying these models to Parkinson’s disease, we propose methods which allows to adapt our approach to other types of data.

Many real-world systems experience deterioration with usage and age, which often leads to low product quality, high production cost, and low system availability. Most previous maintenance and reliability models in the literature do not incorporate condition monitoring information for decision making, which often results in poor failure prediction for partially observable deteriorating systems. For that reason, the development of fault prediction and control scheme using condition-based maintenance techniques has received considerable attention in recent years. This research presents a new framework for predicting failures of a partially observable deteriorating system using Bayesian control techniques. A time series model is fitted to a vector observation process representing partial information about the system state. Residuals are then calculated using the fitted model, which are indicative of system deterioration. The deterioration process is modeled as a 3-state continuous-time homogeneous Markov process. States 0 and 1 are not observable, representing healthy (good) and unhealthy (warning) system operational conditions, respectively. Only the failure state 2 is assumed to be observable. Preventive maintenance can be carried out at any sampling epoch, and corrective maintenance is carried out upon system failure. The form of the optimal control policy that maximizes the long-run expected average availability per unit time has been investigated. It has been proved that a control limit policy is optimal for decision making. The model parameters have been estimated using the Expectation Maximization (EM) algorithm. The optimal Bayesian fault prediction and control scheme, considering long-run average availability maximization along with a practical statistical constraint, has been proposed and compared with the age-based replacement policy. The optimal control limit and sampling interval are calculated in the semi-Markov decision process (SMDP) framework. Another Bayesian fault prediction and control scheme has been developed based on the average run length (ARL) criterion. Comparisons with traditional control charts are provided. Formulae for the mean residual life and the distribution function of system residual life have been derived in explicit forms as functions of a posterior probability statistic. The advantage of the Bayesian model over the well-known 2-parameter Weibull model in system residual life prediction is shown. The methodologies are illustrated using simulated data, real data obtained from the spectrometric analysis of oil samples collected from transmission units of heavy hauler trucks in the mining industry, and vibration data from a planetary gearbox machinery application.