Dynamic Bayesian Collective Awareness Models for a Network of Ego-Things

A novel approach is proposed for multimodal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet-of-Things (IoT) node equipped with machine learning capabilities; CA aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multisensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching dynamic Bayesian network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A growing neural gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely, distributed Markov jump particle filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, first the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analyzed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well

Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles

For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical self-driving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence. This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach. Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure

Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles

Mención Internacional en el título de doctorFor autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical selfdriving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence. This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach. Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Marco Carli.- Secretario: Víctor González Castro.- Vocal: Nicola Conc

Learning probabilistic interaction models

We live in a multi-modal world; therefore it comes as no surprise that the human brain is tailored for the integration of multi-sensory input. Inspired by the human brain, the multi-sensory data is used in Artificial Intelligence (AI) for teaching different concepts to computers. Autonomous Agents (AAs) are AI systems that sense and act autonomously in complex dynamic environments. Such agents can build up Self-Awareness (SA) by describing their experiences through multi-sensorial information with appropriate models and correlating them incrementally with the currently perceived situation to continuously expand their knowledge. This thesis proposes methods to learn such awareness models for AAs. These models include SA and situational awareness models in order to perceive and understand itself (self variables) and its surrounding environment (external variables) at the same time. An agent is considered self-aware when it can dynamically observe and understand itself and its surrounding through different proprioceptive and exteroceptive sensors which facilitate learning and maintaining a contextual representation by processing the observed multi-sensorial data. We proposed a probabilistic framework for generative and descriptive dynamic models that can lead to a computationally efficient SA system. In general, generative models facilitate the prediction of future states while descriptive models enable to select the representation that best fits the current observation. The proposed framework employs a Probabilistic Graphical Models (PGMs) such as Dynamic Bayesian Networks (DBNs) that represent a set of variables and their conditional dependencies. Once we obtain this probabilistic representation, the latter allows the agent to model interactions between itself, as observed through proprioceptive sensors, and the environment, as observed through exteroceptive sensors. In order to develop an awareness system, not only an agent needs to recognize the normal states and perform predictions accordingly, but also it is necessary to detect the abnormal states with respect to its previously learned knowledge. Therefore, there is a need to measure anomalies or irregularities in an observed situation. In this case, the agent should be aware that an abnormality (i.e., a non-stationary condition) never experienced before, is currently present. Due to our specific way of representation, which makes it possible to model multi-sensorial data into a uniform interaction model, the proposed work not only improves predictions of future events but also can be potentially used to effectuate a transfer learning process where information related to the learned model can be moved and interpreted by another body

Designing physical-digital artefacts for the public realm

The exploration of new types of everyday interactions enabled by the increasing integration of digital technologies with the physical world is a major research direction for interaction design research (Dourish, 2004), and a focus on materials and materiality is also of growing significance, e.g.: Internet of Things; interactive architecture; the intersection of craft and technology. Increasingly, designer-researchers from a range of material-focused creative design disciplines are starting to address these themes. Previous studies indicate that new approaches, methods and concepts are required to investigate the evolving field of physical-digital synthesis in the built environment. Addressing this, the thesis asks one central question: What resources for design research can help practitioners and researchers from multiple creative design disciplines improve the design of physical-digital artefacts located in the public realm? A detailed Scoping Study explored experimental research methods for this thesis and produced an overview of physical-digital artefacts in outdoor public space. This scoping influenced the subsequent research: an in-depth field study of the design culture and practices of fifty material-focused designer-researchers; four case studies of physical-digital artefacts in outdoor public spaces; a formative creative design workshop with fourteen participants to test the findings from the research. The chief contribution of this thesis to interaction design research is the development of two resources for design research (the Experiential Framework and the Conceptual Materials for Design Research) and the practical application of these new tools as a method for design research in a simulated ‘real-world’ creative workshop setting. Both resources are intended to co-exist and be integrated with established design research methods and emerging approaches. Hence, the outputs from this thesis are intended to support designer-researchers from a range of creative design backgrounds to conceptualise and design physical-digital artefacts for urban outdoor public spaces that provide richer interaction paradigms for future city dwellers

Imagery and the composition of music: an insight into an original compositional method inspired by mental imagery

This thesis presents a body of eight original musical compositions inspired by the phenomenology of mental imagery, together with a written commentary which describes in depth the compositional process undertaken whilst composing them, defines the concept 'mental imagery' as applied to this process. and sets the concept within a broad theoretical framework which addresses cognitive sciences, the philosophy of meaning and perception, and music historiography. The study codifies a new and original methodology for music composition based on the author's personal account of mental imagery and its influence or permeation into his practice as a composer. The written commentary is structured in two chapters. Chapter One begins with a detailed description of the author's notion of mental imagery, which arose as a natural outcome of his subjective compositional practice. Mental imagery is then compared with ideas, concepts and arguments that address extrinsic elements in music and cross-modal categories in perception. The concept of 'mental imagery' proposed by the author, and therefore the whole compositional process described, is discussed through the lens of the ecological theory of perception and the virtual representation of music, which places mental imagery squarely within contemporary accounts in the field of cognitive sciences and the philosophy of perception.A discussion on the topic of musical meaning follows, addressing arguments that define meaning as a multiform, interdisciplinary concept. Chapter One ends with an insight into music analysis research from the second half of the 20th century, leading to the statement that mental imagery might have been neglected by some music theorists in the recent past. It is argued that this is due to a prevailing epistemological framework that gave priority to formal and technical features of musical material. Chapter Two of this written commentary undertakes a deep and detailed analysis of four of the compositions presented. This analysis gives mental imagery a central role in the descriptive discourse, being sensitive to all the arguments discussed in Chapter One. The analytical style resonates with other accounts such as 'performative analysis' by Nicholas Cook (2002) and 'analog mode of discourse' by John Rahn (1979), and borrows key terms from 'vitality affects' by David Stern (1985). The whole thesis aims to be a valuable example of compositional process inspired by an original, unique and well-described concept: mental imagery. This compositional process codifies new methods or models for compositional practice that may be disseminated to fellow composers. Moreover, the study could also inform performers, theorists and listeners, who may approach their practice in a different light through reflection on the topic of mental imagery and all the associated processes that are here described

Transforming our World through Universal Design for Human Development

An environment, or any building product or service in it, should ideally be designed to meet the needs of all those who wish to use it. Universal Design is the design and composition of environments, products, and services so that they can be accessed, understood and used to the greatest extent possible by all people, regardless of their age, size, ability or disability. It creates products, services and environments that meet people’s needs. In short, Universal Design is good design. This book presents the proceedings of UD2022, the 6th International Conference on Universal Design, held from 7 - 9 September 2022 in Brescia, Italy.The conference is targeted at professionals and academics interested in the theme of universal design as related to the built environment and the wellbeing of users, but also covers mobility and urban environments, knowledge, and information transfer, bringing together research knowledge and best practice from all over the world. The book contains 72 papers from 13 countries, grouped into 8 sections and covering topics including the design of inclusive natural environments and urban spaces, communities, neighborhoods and cities; housing; healthcare; mobility and transport systems; and universally- designed learning environments, work places, cultural and recreational spaces. One section is devoted to universal design and cultural heritage, which had a particular focus at this edition of the conference. The book reflects the professional and disciplinary diversity represented in the UD movement, and will be of interest to all those whose work involves inclusive design