<p>This thesis describes that it is possible to drive synthetic emotions of an interface agent with an electronic nose system developed at AASS. The e-Nose can be used for quality control, and the detected distortion from a known smell sensation prototype is interpreted to a 3D-representation of emotional states, which in turn points to a set of pre-defined muscle contractions. This extension of a rule based motivation system, which we call Facial Expression Driver, is incorporated to a model for sensor fusion with active perception, to provide a general design for a more complex system with additional senses. To be consistent with the biologically inspired sensor fusion model a muscle based animated facial model was chosen as a test bed for the expression of current emotion. The social agent’s facial expressions demonstrate its tolerance to the detected distortion in order to manipulate the user to restore the system to functional balance. Only a few of the known projects use chemically based sensing to drive a face in real-time, whether they are virtual characters or animatronics. This work may inspire a future android implementation of a head with electro active polymers as synthetic facial muscles.</p>

<p> <p>&nbsp / </p> </p> <p align="left">The underlying theme of this thesis is identification, classification, detection and prediction of cellular network faults using state of the art technologies, methods and algorithms.</p>

Post-Cold War military conflicts have highlighted the need for a flexible, agile joint force responsive to emerging crises around the globe. The 2005 Joint Capabilities Integration and Development System (JCIDS) acquisition policy document mandates a shift away from stove-piped threat-based acquisition to a capability-based model focused on the multiple ways and means of achieving an effect. This shift requires a greater emphasis on scenarios, tactics, and operational concepts during the conceptual phase of design and structured processes for technology evaluation to support this transition are lacking. In this work, a methodology for quantitative technology evaluation for systems-of-systems is defined. Physics-based models of an aircraft system are exercised within a hierarchical, object-oriented constructive simulation to quantify technology potential in the context of a relevant scenario. A major technical challenge to this approach is the lack of resources to support real-time human-in-the-loop tactical decision making and technology analysis. An approach that uses intelligent agents to create a "Meta-General" capable of forecasting strategic and tactical decisions based on technology inputs is used. To demonstrate the synergy between new technologies and tactics, surrogate models are utilized to provide intelligence to individual agents within the framework and develop a set of tactics that appropriately exploit new technologies. To address the long run-times associated with constructive military simulations, neural network surrogate models are implemented around the forecasting environment to enable rapid trade studies. Probabilistic techniques are used to quantify uncertainty and richly populate the design space with technology-infused alternatives. Since a large amount of data is produced in the analysis of systems-of-systems, dynamic, interactive visualization techniques are used to enable "what-if" games on assumptions, systems, technologies, tactics, and evolving threats. The methodology developed in this dissertation is applied to a notional Long Range Strike air vehicle and system architecture in the context of quantitative technology evaluation for the United States Air Force.

Can a robot understand a human's social behavior? Moreover, how should a robot act in response to a human's behavior? If the goals of artificial intelligence are to understand, imitate, and interact with human level intelligence then researchers must also explore the social underpinnings of this intellect. Our endeavor is buttressed by work in biology, neuroscience, social psychology and sociology. Initially developed by Kelley and Thibaut, social psychology's interdependence theory serves as a conceptual skeleton for the study of social situations, a computational process of social deliberation, and relationships (Kelley&Thibaut, 1978). We extend and expand their original work to explore the challenge of interaction with an embodied, situated robot. This dissertation investigates the use of outcome matrices as a means for computationally representing a robot's interactions. We develop algorithms that allow a robot to create these outcome matrices from perceptual information and then to use them to reason about the characteristics of their interactive partner. This work goes on to introduce algorithms that afford a means for reasoning about a robot's relationships and the trustworthiness of a robot's partners. Overall, this dissertation embodies a general, principled approach to human-robot interaction which results in a novel and scientifically meaningful approach to topics such as trust and relationships.

Arctic regions are thought to be more sensitive to climate change fluctuations, making weather data from these regions more valuable for climate modeling. Scientists have expressed an interest in deploying a robotic sensor network in these areas, minimizing the exposure of human researchers to the harsh environment, while allowing dense, targeted data collection to commence. For any such robotic system to be successful, a certain set of base navigational functionality must be developed. Further, these navigational algorithms must rely on the types of low-cost sensors that would be viable for use in a multi-agent system. A set of vision-based processing techniques have been proposed, which augment current robotic technologies for use in glacial terrains. Specifically, algorithms for estimating terrain traversability, robot localization, and terrain reconstruction have been developed which use data collected exclusively from a single camera and other low-cost robotic sensors. For traversability assessment, a custom algorithm was developed that uses local scale surface texture to estimate the terrain slope. Additionally, a horizon line estimation system has been proposed that is capable of coping with low-contrast, ambiguous horizons. For localization, a monocular simultaneous localization and mapping (SLAM) filter has been fused with consumer-grade GPS measurements to produce full robot pose estimates that do not drift over long traverses. Finally, a terrain reconstruction methodology has been proposed that uses a Gaussian process framework to incorporate sparse SLAM landmarks with dense slope estimates to produce a single, consistent terrain model. These algorithms have been tested within a custom glacial terrain computer simulation and against multiple data sets acquired during glacial field trials. The results of these tests indicate that vision is a viable sensing modality for autonomous glacial robotics, despite the obvious challenges presented by low-contrast glacial scenery. The findings of this work are discussed within the context of the larger arctic sensor network project, and a direction for future work is recommended.

Σκοπός της διατριβής είναι η ανάλυση, η μελέτη και η μοντελοποίηση της συμπεριφοράς τόσο των ευφυών πρακτόρων όσο και των χρηστών σε εικονικά περιβάλλοντα μάθησης, με τη χρήση τεχνικών υπολογιστικής νοημοσύνης. Το θεματικό αντικείμενο της διδακτορικής διατριβής αποτελεί ένα σύγχρονο αντικείμενο βασικής έρευνας με μεγάλο εύρος πρακτικών εφαρμογών. Η βάση της ερευνητικής δραστηριότητας εστιάζεται σε δύο βασικούς τομείς: 1. Προσαρμόσιμη μοντελοποίηση συμπεριφορών ευφυών πρακτόρων σε εικονικά περιβάλλοντα μάθησης, σύμφωνα με κανόνες βελτιστοποίησης της μαθησιακής επίδρασης στο χρήστη μέσα στο εικονικό περιβάλλον μάθησης. 2. Μοντελοποίηση χρηστών εικονικών περιβαλλόντων μάθησης, με στόχο τη βελτιστοποίηση της μαθησιακής επίδρασης στο χρήστη. Για τη μοντελοποίηση, τόσο της συμπεριφοράς των ευφυών πρακτόρων, όσο και των χρηστών, χρησιμοποιήθηκαν προηγμένες τεχνικές υπολογιστικής νοημοσύνης (Bayesian Δίκτυα, Γενετικοί και Εξελικτικοί Αλγόριθμοι). Αυτές οι τεχνικές, εκτός από την ευφυΐα, ενσωματώνουν και το επιθυμητό χαρακτηριστικό της προσαρμοσιμότητας, με την έννοια ότι μπορούν να προσαρμόζονται στις αλλαγές του περιβάλλοντος. Τα παραπάνω αποτελέσματα αξιολογήθηκαν στη χρήση τους σε Ευφυή Εικονικά Συστήματα Μάθησης βασισμένα στο Web (Intelligent Virtual Learning Systems – IVLS), τα οποία αποτελούν ουσιαστικά το μέσον εξαγωγής συμπερασμάτων και υποστηρικτικού υλικού για τη μετρήσιμη συμπεριφορά τόσο των ευφυών πρακτόρων όσο και των χρηστών, μέσα σε τέτοια περιβάλλοντα. / The main objectives of the thesis are the analysis, study and the provision of a behavior modeling procedure of the intelligent agents and the students in virtual e-learning systems using computational intelligence techniques. The domain of the thesis is a topic of basic research with a large scale of applied results. The basis of the research is focused in two main sectors: 1. Adaptive behavior modeling of intelligent agents in virtual learning systems, according to specific optimization rules of the learning process during the interaction of the user/student with the e-learning environment. 2. User modeling of the users of virtual learning environments towards the optimization of the learning process. For the modeling procedure of the behavior of intelligent agents and of the users specific computational intelligence techniques have been applied (Bayesian Networks, Genetic και Evolutionary Algorithms). The specific techniques provide intelligence to the system and the most important the feature of adaptability. The aforementioned results have been evaluated on Intelligent Virtual Learning Systems, which constitute the medium for the inference of the results and the mean for supportive material for the measurable behavior of the intelligent agents and of the users in Intelligent Virtual Learning Systems.

Οι τεχνικές Υπολογιστικής Νοημοσύνης βρίσκουν σε μεγάλο βαθμό εφαρμογή σε Ηλεκτρονικά Συστήματα Μάθησης. Στην εργασία αυτή υιοθετείται η τεχνική των Bayesian δικτύων. Αναλυτικότερα υλοποιείται ένα έξυπνο σύστημα το οποίο αναλαμβάνει τη διαχείριση των ερωτηματολογίων ενός Ηλεκτρονικού Συστήματος Μάθησης. Σκοπός της των Bayesian δικτύων είναι η «έξυπνη» διαχείριση των ερωτηματολογίων. Πιο συγκεκριμένα, πραγματοποιείται γραφική απεικόνιση των ερωτηματολογίων σε Bayesian γράφημα όπου κάθε ερώτηση αντιστοιχεί σε ένα κόμβο του γραφήματος. Στο γράφημα αυτό εφαρμόζονται οι εξισώσεις του Bayes σε κάθε κόμβο του γραφήματος ώστε να υπολογιστούν οι πιθανότητες επιτυχούς απάντησης μιας ερώτησης. Στη συνέχεια οι πιθανότητες συγκρίνονται με κατώφλια τα οποία ορίζει ο διαχειριστής του συστήματος ώστε να αποφευχθούν ερωτήσεις στις οποίες ο χρήστης έχει μεγάλη πιθανότητα να απαντήσει επιτυχώς. Επίτευγμα αυτής της υλοποίησης είναι η εξοικονόμηση ερωτήσεων και χρόνου εκ μέρους του χρήστη. Το δεύτερο μέρος της εργασίας αφορά στην επέκταση του παραπάνω συστήματος χρησιμοποιώντας την αρχιτεκτονική ευφυών πρακτόρων. Βασικός σκοπός της επέκτασης αυτής είναι η δυνατότητα διαχείρισης ενός μεγάλου αριθμού χρηστών και ερωτηματολογίων από απομακρυσμένα συστήματα. / In this contribution an innovative platform is being presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

Automation has become increasingly prevalent in all forms of society. Activities that are too difficult for a human or to dangerous can be done by machines which do not share those downsides. In addition, tasks can be scheduled more precisely and accurately. Increases in the autonomy have allowed for a new level of tasks which are completed by teams of automated agents rather than a single one, called cooperative control. This has many benefits; but comes at the cost of increased complexity and coordination. The main thrust of research in this field is problem based, considering communication issues as a secondary feature. There is a gap considering problems in which many changes occur as rapidly as communication and the issues that arise as a result. This is the main motivation. This research presents an approach to cooperative control in highly variable systems and tackles some of the issues present in such a system. One of the most important issues is the communication network itself, which is used as an indicator for how healthy the system is an how well it may react to future changes. Therefore using the network as an input to control allows the system to navigate between conservative and aggressive techniques to improve performance while still maintaining robustness. Results are based on a test bed designed to simulate a wide variety of problem types based on: network type; numbers of actors; frequency of changes; impact of changes and method of change. The developed control method is compared to the baseline case ignoring cooperation as well as an idealized case assuming perfect system knowledge. The baseline represents sacrifices coordination to achieve a high level of robustness at reduced performance while the idealized case represents the best possible performance. The control techniques developed give a performance at least as good as the baseline case if not better for all simulations.

<p> <p>&nbsp / </p> </p> <p align="left">The underlying theme of this thesis is identification, classification, detection and prediction of cellular network faults using state of the art technologies, methods and algorithms.</p>

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal