Machine Body Language: Expressing a Smart Speaker’s Activity with Intelligible Physical Motion

People’s physical movement and body language implicitly convey what they think and feel, are doing or are about to do. In contrast, current smart speakers miss out on this richness of body language, primarily relying on voice commands only. We present QUBI, a dynamic smart speaker that leverages expressive physical motion – stretching, nodding, turning, shrugging, wiggling, pointing and leaning forwards/backwards – to convey cues about its underlying behaviour and activities. We conducted a qualitative Wizard of Oz lab study, in which 12 participants interacted with QUBI in four scripted scenarios. From our study, we distilled six themes: (1) mirroring and mimicking motions; (2) body language to supplement voice instructions; (3) anthropomorphism and personality; (4) audio can trump motion; (5) reaffirming uncertain interpretations to support mutual understanding; and (6) emotional reactions to QUBI’s behaviour. From this, we discuss design implications for future smart speakers

Feel, Don\u27t Think Review of the Application of Neuroscience Methods for Conversational Agent Research

Conversational agents (CAs) equipped with human-like features (e.g., name, avatar) have been reported to induce the perception of humanness and social presence in users, which can also increase other aspects of users’ affection, cognition, and behavior. However, current research is primarily based on self-reported measurements, leaving the door open for errors related to the self-serving bias, socially desired responding, negativity bias and others. In this context, applying neuroscience methods (e.g., EEG or MRI) could provide a means to supplement current research. However, it is unclear to what extent such methods have already been applied and what future directions for their application might be. Against this background, we conducted a comprehensive and transdisciplinary review. Based on our sample of 37 articles, we find an increased interest in the topic after 2017, with neural signal and trust/decision-making as upcoming areas of research and five separate research clusters, describing current research trends

Vision-Based Object Recognition and 3-D Pose Estimation Using Conic Features

This thesis deals with monocular vision-based object recognition and 3-D pose estimation based on conic features. Conic features including circles and ellipses are frequently observed in many man-made objects in real word as well as have the merit of robustness potentially in feature extraction in vision-based applications. Although the 3-D pose estimation problem of conic features in 3-D space has been studied well since 1990, the previous work has not provided a unique solution completely for full 3-D pose parameters (i.e., 3-orientations and 3-positions) due to complexity from high nonlinearity of a general conic. This thesis, therefore, renews conic features in a new perspective on geometric invariants in both 3-D space and 2-D projective space, incorporating other geometric features with conics. First, as the most essential step in dealing with conics, this thesis shows that the pose parameters of a circular feature in 3-D space can be derived analytically from incorporating a coplanar point. A procedure of pose parameter recovery is described in detail, and its performance is evaluated and discussed in view of pose estimation errors and sensitivity. Second, it is also revealed that the pose of an elliptic feature can be resolved when two coplanar points are incorporated on the basis of the polarity of two points for a conic in 2-D projective space. This thesis proposes a series of algorithms to determine the 3-D pose parameters uniquely, and evaluates the proposed method through a measure of estimation performance and sensitivity depending on point locations. Third, a pair of two conics is dealt with, which is regarded as an extension of the idea of the incorporation scheme to another conic feature from point features. Under the polarity concept, this thesis proves that the problem involving a pair of two conics can be formulated with the problem of one ellipse with two points so that its solution is derived in the same form as in the ellipse case. In order to treat two or more conic objects as well as to deal with an object recognition problem, the rest of thesis concentrates on the theoretical foundation of multiple object recognition. First, some effective modeling approaches are described. A general object model is specially designed to model multiple objects for object recognition and pose recovery in view of spatial geometry. In particular, this thesis defines a pairwise conic model that can describes the geometrical relation between two conics invariantly in 2-D projective space, which consists of a pairwise conic (PC), a pairwise conic invariant (PCI), and a pairwise conic pole (PCP). Based on the two kinds of models, an object learning and recognition system is proposed as a general framework for multiple object recognition. Considering simplicity and flexibility in object learning stage, this thesis introduces a semi-automatic learning scheme to construct the multiple object model from a model image at once. To utilize geometric relations among multiple objects effectively in object recognition, this thesis specifies some feature functions based on the pairwise conic model, and then describes an object recognition method in a fashion of linear-chain conditional random field (CRF). In particular, as a post refinement step of the recognition, a geometric alignment procedure is also proposed in algorithmic details to improve recognition performance against noisy conditions. Last, the multiple object recognition method is evaluated intensively through two practical applications that deal with a place recognition and an elevator button recognition problem for service robots. A series of experiment results supports the effectiveness of the proposed method, maintaining reliable performance against noisy conditions in the presence of perspective distortion and partial object occlusions.Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Research objective and expected contribution . . . . . . . . . . . . . . . . . . 6 1.4 Organization of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 3-D Pose Estimation of a Circular Feature 10 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.4 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 Preliminaries: an elliptic cone in 3-D space and its homogeneous representation in 2-D projective space . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Homogeneous representation . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Principal planes of a cone versus diagonalization of a conic matrix Q . 16 2.3 3-D interpretation of a circular feature for 3-D pose estimation . . . . . . . . 19 2.3.1 3-D orientation estimation . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 3-D position estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.3 Composition of homogeneous transformation and discrimination for the unique solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4 Experiment results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4.1 A numerical example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4.2 Evaluation of pose estimation performance . . . . . . . . . . . . . . . 29 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 3-D Pose Estimation of an Elliptic Feature 35 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.1.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2 Interpretation of an elliptic feature with coplanar points in 2-D projective space 38 3.2.1 The minimal number of points for pose estimation . . . . . . . . . . . 39 3.2.2 Analysis of possible constraints for relative positions of two points to an ellipse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.3 Feature selection scheme for stable homography estimation . . . . . . 43 3.3 3-D pose estimation algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.3.1 Extraction of triangular features from an elliptic object . . . . . . . . 47 3.3.2 Homography decomposition . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3.3 Composition of homogeneous transformation matrix with unique solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4 Experiment results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.2 Evaluation of the proposed method . . . . . . . . . . . . . . . . . . . . 54 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4 3-D Pose Estimation of a Pair of Conic Features 61 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2 3-D pose estimation of a conic feature incorporated with line features . . . . 61 4.3 3-D pose estimation of a conic feature incorporated with another conic feature 63 4.3.1 Some examples of self-polar triangle and invariants . . . . . . . . . . . 65 4.3.2 3-D pose estimation of a pair of coplanar conics . . . . . . . . . . . . . 67 4.3.3 Examples of 3-D pose estimation of a conic feature incorporated with another conic feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Multiple Object Recognition Based on Pairwise Conic Model 77 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.2 Learning of geometric relation of multiple objects . . . . . . . . . . . . . . . . 78 5.3 Pairwise conic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.3.1 De_nitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.4 Multiple object recognition based on pairwise conic model and conditional random _elds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.4.1 Graphical model for multiple object recognition . . . . . . . . . . . . . 86 5.4.2 Linear-chain conditional random _eld . . . . . . . . . . . . . . . . . . 87 5.4.3 Determination of low-level feature functions for multiple object recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.4.4 Range selection trick for e_ciently computing the costs of low-level feature functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.4.5 Evaluation of observation sequence . . . . . . . . . . . . . . . . . . . . 93 5.4.6 Object recognition based on hierarchical CRF . . . . . . . . . . . . . . 95 5.5 Geometric alignment algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 6 Application to Place Recognition for Service Robots 105 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 6.2 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.2.1 Detection of 2-D geometric shapes . . . . . . . . . . . . . . . . . . . . 107 6.2.2 Examples of shape feature extraction . . . . . . . . . . . . . . . . . . . 109 6.3 Object modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.3.1 A place model that describes multiple landmark objects . . . . . . . . 112 6.3.2 Pairwise conic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.3.3 Incorporation of non-conic features with a pairwise conic model . . . . 114 6.4 Place learning and recognition system . . . . . . . . . . . . . . . . . . . . . . 121 6.4.1 HCRF-based recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.5 Experiment results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.5.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.5.2 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7 Application to Elevator Button Recognition 136 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 7.1.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 7.2 Object modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 7.2.1 Geometric model for multiple button objects . . . . . . . . . . . . . . 140 7.2.2 Pairwise conic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.3 Learning and recognition system . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.3.1 Button object learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7.3.2 CRF-based recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 7.4 Experiment results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 7.4.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 7.4.2 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 151 7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 8 Concluding remarks 159 8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 8.2 Further work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 References 161 Summary (in Korean) 16

Autonomous behaviour in tangible user interfaces as a design factor

PhD ThesisThis thesis critically explores the design space of autonomous and actuated artefacts, considering how autonomous behaviours in interactive technologies might shape and influence users’ interactions and behaviours. Since the invention of gearing and clockwork, mechanical devices were built that both fascinate and intrigue people through their mechanical actuation. There seems to be something magical about moving devices, which draws our attention and piques our interest. Progress in the development of computational hardware is allowing increasingly complex commercial products to be available to broad consumer-markets. New technologies emerge very fast, ranging from personal devices with strong computational power to diverse user interfaces, like multi-touch surfaces or gestural input devices. Electronic systems are becoming smaller and smarter, as they comprise sensing, controlling and actuation. From this, new opportunities arise in integrating more sensors and technology in physical objects. These trends raise some specific questions around the impacts smarter systems might have on people and interaction: how do people perceive smart systems that are tangible and what implications does this perception have for user interface design? Which design opportunities are opened up through smart systems? There is a tendency in humans to attribute life-like qualities onto non-animate objects, which evokes social behaviour towards technology. Maybe it would be possible to build user interfaces that utilise such behaviours to motivate people towards frequent use, or even motivate them to build relationships in which the users care for their devices. Their aim is not to increase the efficiency of user interfaces, but to create interfaces that are more engaging to interact with and excite people to bond with these tangible objects. This thesis sets out to explore autonomous behaviours in physical interfaces. More specifically, I am interested in the factors that make a user interpret an interface as autonomous. Through a review of literature concerned with animated objects, autonomous technology and robots, I have mapped out a design space exploring the factors that are important in developing autonomous interfaces. Building on this and utilising workshops conducted with other researchers, I have vi developed a framework that identifies key elements for the design of Tangible Autonomous Interfaces (TAIs). To validate the dimensions of this framework and to further unpack the impacts on users of interacting with autonomous interfaces I have adopted a ‘research through design’ approach. I have iteratively designed and realised a series of autonomous, interactive prototypes, which demonstrate the potential of such interfaces to establish themselves as social entities. Through two deeper case studies, consisting of an actuated helium balloon and desktop lamp, I provide insights into how autonomy could be implemented into Tangible User Interfaces. My studies revealed that through their autonomous behaviour (guided by the framework) these devices established themselves, in interaction, as social entities. They furthermore turned out to be acceptable, especially if people were able to find a purpose for them in their lives. This thesis closes with a discussion of findings and provides specific implications for design of autonomous behaviour in interfaces

Apprentissage statistique de modèles de comportement multimodal pour les agents conversationnels interactifs

Face to face interaction is one of the most fundamental forms of human communication. It is a complex multimodal and coupled dynamic system involving not only speech but of numerous segments of the body among which gaze, the orientation of the head, the chest and the body, the facial and brachiomanual movements, etc. The understanding and the modeling of this type of communication is a crucial stage for designing interactive agents capable of committing (hiring) credible conversations with human partners. Concretely, a model of multimodal behavior for interactive social agents faces with the complex task of generating gestural scores given an analysis of the scene and an incremental estimation of the joint objectives aimed during the conversation. The objective of this thesis is to develop models of multimodal behavior that allow artificial agents to engage into a relevant co-verbal communication with a human partner. While the immense majority of the works in the field of human-agent interaction (HAI) is scripted using ruled-based models, our approach relies on the training of statistical models from tracks collected during exemplary interactions, demonstrated by human trainers. In this context, we introduce "sensorimotor" models of behavior, which perform at the same time the recognition of joint cognitive states and the generation of the social signals in an incremental way. In particular, the proposed models of behavior have to estimate the current unit of interaction ( IU) in which the interlocutors are jointly committed and to predict the co-verbal behavior of its human trainer given the behavior of the interlocutor(s). The proposed models are all graphical models, i.e. Hidden Markov Models (HMM) and Dynamic Bayesian Networks (DBN). The models were trained and evaluated - in particular compared with classic classifiers - using datasets collected during two different interactions. Both interactions were carefully designed so as to collect, in a minimum amount of time, a sufficient number of exemplars of mutual attention and multimodal deixis of objects and places. Our contributions are completed by original methods for the interpretation and comparative evaluation of the properties of the proposed models. By comparing the output of the models with the original scores, we show that the HMM, thanks to its properties of sequential modeling, outperforms the simple classifiers in term of performances. The semi-Markovian models (HSMM) further improves the estimation of sensorimotor states thanks to duration modeling. Finally, thanks to a rich structure of dependency between variables learnt from the data, the DBN has the most convincing performances and demonstrates both the best performance and the most faithful multimodal coordination to the original multimodal events.L'interaction face-à-face représente une des formes les plus fondamentales de la communication humaine. C'est un système dynamique multimodal et couplé – impliquant non seulement la parole mais de nombreux segments du corps dont le regard, l'orientation de la tête, du buste et du corps, les gestes faciaux et brachio-manuels, etc – d'une grande complexité. La compréhension et la modélisation de ce type de communication est une étape cruciale dans le processus de la conception des agents interactifs capables d'engager des conversations crédibles avec des partenaires humains. Concrètement, un modèle de comportement multimodal destiné aux agents sociaux interactifs fait face à la tâche complexe de générer un comportement multimodal étant donné une analyse de la scène et une estimation incrémentale des objectifs conjoints visés au cours de la conversation. L'objectif de cette thèse est de développer des modèles de comportement multimodal pour permettre aux agents artificiels de mener une communication co-verbale pertinente avec un partenaire humain. Alors que l'immense majorité des travaux dans le domaine de l'interaction humain-agent repose essentiellement sur des modèles à base de règles, notre approche se base sur la modélisation statistique des interactions sociales à partir de traces collectées lors d'interactions exemplaires, démontrées par des tuteurs humains. Dans ce cadre, nous introduisons des modèles de comportement dits "sensori-moteurs", qui permettent à la fois la reconnaissance des états cognitifs conjoints et la génération des signaux sociaux d'une manière incrémentale. En particulier, les modèles de comportement proposés ont pour objectif d'estimer l'unité d'interaction (IU) dans laquelle sont engagés de manière conjointe les interlocuteurs et de générer le comportement co-verbal du tuteur humain étant donné le comportement observé de son/ses interlocuteur(s). Les modèles proposés sont principalement des modèles probabilistes graphiques qui se basent sur les chaînes de markov cachés (HMM) et les réseaux bayésiens dynamiques (DBN). Les modèles ont été appris et évalués – notamment comparés à des classifieurs classiques – sur des jeux de données collectés lors de deux différentes interactions face-à-face. Les deux interactions ont été soigneusement conçues de manière à collecter, en un minimum de temps, un nombre suffisant d'exemplaires de gestion de l'attention mutuelle et de deixis multimodale d'objets et de lieux. Nos contributions sont complétées par des méthodes originales d'interprétation et d'évaluation des propriétés des modèles proposés. En comparant tous les modèles avec les vraies traces d'interactions, les résultats montrent que le modèle HMM, grâce à ses propriétés de modélisation séquentielle, dépasse les simples classifieurs en terme de performances. Les modèles semi-markoviens (HSMM) ont été également testé et ont abouti à un meilleur bouclage sensori-moteur grâce à leurs propriétés de modélisation des durées des états. Enfin, grâce à une structure de dépendances riche apprise à partir des données, le modèle DBN a les performances les plus probantes et démontre en outre la coordination multimodale la plus fidèle aux évènements multimodaux originaux

Apprentissage statistique de modèles de comportement multimodal pour les agents conversationnels interactifs

Face to face interaction is one of the most fundamental forms of human communication. It is a complex multimodal and coupled dynamic system involving not only speech but of numerous segments of the body among which gaze, the orientation of the head, the chest and the body, the facial and brachiomanual movements, etc. The understanding and the modeling of this type of communication is a crucial stage for designing interactive agents capable of committing (hiring) credible conversations with human partners. Concretely, a model of multimodal behavior for interactive social agents faces with the complex task of generating gestural scores given an analysis of the scene and an incremental estimation of the joint objectives aimed during the conversation. The objective of this thesis is to develop models of multimodal behavior that allow artificial agents to engage into a relevant co-verbal communication with a human partner. While the immense majority of the works in the field of human-agent interaction (HAI) is scripted using ruled-based models, our approach relies on the training of statistical models from tracks collected during exemplary interactions, demonstrated by human trainers. In this context, we introduce "sensorimotor" models of behavior, which perform at the same time the recognition of joint cognitive states and the generation of the social signals in an incremental way. In particular, the proposed models of behavior have to estimate the current unit of interaction ( IU) in which the interlocutors are jointly committed and to predict the co-verbal behavior of its human trainer given the behavior of the interlocutor(s). The proposed models are all graphical models, i.e. Hidden Markov Models (HMM) and Dynamic Bayesian Networks (DBN). The models were trained and evaluated - in particular compared with classic classifiers - using datasets collected during two different interactions. Both interactions were carefully designed so as to collect, in a minimum amount of time, a sufficient number of exemplars of mutual attention and multimodal deixis of objects and places. Our contributions are completed by original methods for the interpretation and comparative evaluation of the properties of the proposed models. By comparing the output of the models with the original scores, we show that the HMM, thanks to its properties of sequential modeling, outperforms the simple classifiers in term of performances. The semi-Markovian models (HSMM) further improves the estimation of sensorimotor states thanks to duration modeling. Finally, thanks to a rich structure of dependency between variables learnt from the data, the DBN has the most convincing performances and demonstrates both the best performance and the most faithful multimodal coordination to the original multimodal events.L'interaction face-à-face représente une des formes les plus fondamentales de la communication humaine. C'est un système dynamique multimodal et couplé – impliquant non seulement la parole mais de nombreux segments du corps dont le regard, l'orientation de la tête, du buste et du corps, les gestes faciaux et brachio-manuels, etc – d'une grande complexité. La compréhension et la modélisation de ce type de communication est une étape cruciale dans le processus de la conception des agents interactifs capables d'engager des conversations crédibles avec des partenaires humains. Concrètement, un modèle de comportement multimodal destiné aux agents sociaux interactifs fait face à la tâche complexe de générer un comportement multimodal étant donné une analyse de la scène et une estimation incrémentale des objectifs conjoints visés au cours de la conversation. L'objectif de cette thèse est de développer des modèles de comportement multimodal pour permettre aux agents artificiels de mener une communication co-verbale pertinente avec un partenaire humain. Alors que l'immense majorité des travaux dans le domaine de l'interaction humain-agent repose essentiellement sur des modèles à base de règles, notre approche se base sur la modélisation statistique des interactions sociales à partir de traces collectées lors d'interactions exemplaires, démontrées par des tuteurs humains. Dans ce cadre, nous introduisons des modèles de comportement dits "sensori-moteurs", qui permettent à la fois la reconnaissance des états cognitifs conjoints et la génération des signaux sociaux d'une manière incrémentale. En particulier, les modèles de comportement proposés ont pour objectif d'estimer l'unité d'interaction (IU) dans laquelle sont engagés de manière conjointe les interlocuteurs et de générer le comportement co-verbal du tuteur humain étant donné le comportement observé de son/ses interlocuteur(s). Les modèles proposés sont principalement des modèles probabilistes graphiques qui se basent sur les chaînes de markov cachés (HMM) et les réseaux bayésiens dynamiques (DBN). Les modèles ont été appris et évalués – notamment comparés à des classifieurs classiques – sur des jeux de données collectés lors de deux différentes interactions face-à-face. Les deux interactions ont été soigneusement conçues de manière à collecter, en un minimum de temps, un nombre suffisant d'exemplaires de gestion de l'attention mutuelle et de deixis multimodale d'objets et de lieux. Nos contributions sont complétées par des méthodes originales d'interprétation et d'évaluation des propriétés des modèles proposés. En comparant tous les modèles avec les vraies traces d'interactions, les résultats montrent que le modèle HMM, grâce à ses propriétés de modélisation séquentielle, dépasse les simples classifieurs en terme de performances. Les modèles semi-markoviens (HSMM) ont été également testé et ont abouti à un meilleur bouclage sensori-moteur grâce à leurs propriétés de modélisation des durées des états. Enfin, grâce à une structure de dépendances riche apprise à partir des données, le modèle DBN a les performances les plus probantes et démontre en outre la coordination multimodale la plus fidèle aux évènements multimodaux originaux

Socio-Informatics

Contents Editorial Thematic Focus: Socio-Informatics Introduction to the Thematic Focus “Socio-Informatics” / Claudia Müller Digitalisation in Small German Metal-Working Companies. Appropriation of Technology in a “Traditional” Industrial Domain / Bernhard Nett, Jennifer Bönsch Travelling by Taxi Brousse in Madagascar: An Investigation into Practices of Overland Transportation / Volker Wulf, Kaoru Misaki, Dave Randall, and Markus Rohde Mobile and Interactive Media in the Store? Design Case Study on Bluetooth Beacon Concepts for Food Retail / Christian Reuter, Inken Leopold Facebook and the Mass Media in Tunisia / Konstantin Aal, Marén Schorch, Esma Ben Hadj Elkilani, Volker Wulf Book Review Symposium Charles Goodwin Charles Goodwin’s Co-Operative Action: The Idea and the Argument / Erhard Schüttpelz, Christian Meyer Multi-Modal Interaction and Tool-Making: Goodwin’s Intuition / Christian Meyer, Erhard Schüttpelz Co-Operation is a Feature of Sociality, not an Attribute of People : “We inhabit each other’s actions.” (Goodwin, cover) / Jutta Wiesemann, Klaus Amann The Making of the World in Co-Operative Action. From Sentence Construction to Cultural Evolution / Jürgen Streeck On Goodwin and his Co-Operative Action / Jörg R. Bergman

INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION

This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems

Intelligence by Design: Principles of Modularity and Coordination for Engineerin

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation describes an approach, Behavior-Oriented Design (BOD) for engineering complex agents. A complex agent is one that must arbitrate between potentially conflicting goals or behaviors. Behavior-oriented design builds on work in behavior-based and hybrid architectures for agents, and the object oriented approach to software engineering. The primary contributions of this dissertation are: 1.The BOD architecture: a modular architecture with each module providing specialized representations to facilitate learning. This includes one pre-specified module and representation for action selection or behavior arbitration. The specialized representation underlying BOD action selection is Parallel-rooted, Ordered, Slip-stack Hierarchical (POSH) reactive plans. 2.The BOD development process: an iterative process that alternately scales the agent's capabilities then optimizes the agent for simplicity, exploiting tradeoffs between the component representations. This ongoing process for controlling complexity not only provides bias for the behaving agent, but also facilitates its maintenance and extendibility. The secondary contributions of this dissertation include two implementations of POSH action selection, a procedure for identifying useful idioms in agent architectures and using them to distribute knowledge across agent paradigms, several examples of applying BOD idioms to established architectures, an analysis and comparison of the attributes and design trends of a large number of agent architectures, a comparison of biological (particularly mammalian) intelligence to artificial agent architectures, a novel model of primate transitive inference, and many other examples of BOD agents and BOD development