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

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

Modeling contaminant transport and fate and subsequent impacts on ecosystems

Assessing risks associated with the release of metals into the environment and managing remedial activities requires simulation tools that depict speciation and risk with accurate mechanistic models and well-defined transport parameters. Such tools need to address the following processes: (1) aqueous speciation, (2) distribution mechanisms, (3) transport, and (4) ecological risk. The primary objective of this research is to develop a simulation tool that accounts for these processes. Speciation in the aqueous phase can be assessed with geochemical equilibrium models, such as MINEQL+. Furthermore, metal distribution can be addressed mechanistically. Studies with Pb sorption to amorphous aluminum (HAG), iron (HFO), and manganese (HMO) oxides, as well as oxide coatings, demonstrated that intraparticle diffusion is the rate-limiting mechanism in the sorption process, where best-fit surface diffusivities ranged from 10-18 to 10-15 cm2 s-1 Intraparticle surface diffusion was incorporated into the Groundwater Modeling System (GMS) to accurately simulate metal contaminant mobility where oxides are present. In the model development, the parabolic concentration layer approximation and the operator split technique were used to solve the microscopic diffusion equation coupled with macroscopic advection and dispersion. The resulting model was employed for simulating Sr90 mobility at the U.S. Department of Energy (DOE) Hanford Site. The Sr90 plume is observed to be migrating out of the 100-N area extending into other areas of the Hanford Site and beyond. Once bioavailability is understood, static or dynamic ecological risk assessments can be conducted. Employing the ERA model, a static ecological risk assessment for exposure to depleted uranium (DU) at Aberdeen and Yuma Proving Grounds (APG and YPG) revealed that a reduction in plant root weight is considered likely to occur. For most terrestrial animals at YPG, the predicted DU dose is less than that which would result in a decrease in offspring. However, for the lesser long-nosed bat, reproductive effects are expected to occur through the reduction in size and weight of offspring. At APG, based on very limited data, it is predicted that uranium uptake will not likely affect survival of terrestrial animals and aquatic species. In model validation, sampling of pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil showed that body burden concentrations fall into the distributions simulated at both sites. This static risk assessment provides a solid background for applying the dynamic approach. Overall, this research contributes to a holistic approach in developing accurate mechanistic models for simulating metal contaminant mobility and bioavailability in subsurface environments

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

World Modeling for Intelligent Autonomous Systems

Within the scope of this work, we have attained a row of theoretical and experimental results in the field of world modeling as well as gathered significant experience and expertise. The covered topics include concepts and approaches for dynamic and prior knowledge modeling, information association, fusion and management as well as their practical realization and experimental evaluation

New Game Physics - Added Value for Transdisciplinary Teams

This study focused on game physics, an area of computer game design where physics is applied in interactive computer software. The purpose of the research was a fresh analysis of game physics in order to prove that its current usage is limited and requires advancement. The investigations presented in this dissertation establish constructive principles to advance game physics design. The main premise was that transdisciplinary approaches provide significant value. The resulting designs reflected combined goals of game developers, artists and physicists and provide novel ways to incorporate physics into games. The applicability and user impact of such new game physics across several target audiences was thoroughly examined. In order to explore the transdisciplinary nature of the premise, valid evidence was gathered using a broad range of theoretical and practical methodologies. The research established a clear definition of game physics within the context of historical, technological, practical, scientific, and artistic considerations. Game analysis, literature reviews and seminal surveys of game players, game developers and scientists were conducted. A heuristic categorization of game types was defined to create an extensive database of computer games and carry out a statistical analysis of game physics usage. Results were then combined to define core principles for the design of unconventional new game physics elements. Software implementations of several elements were developed to examine the practical feasibility of the proposed principles. This research prototype was exposed to practitioners (artists, game developers and scientists) in field studies, documented on video and subsequently analyzed to evaluate the effectiveness of the elements on the audiences. The findings from this research demonstrated that standard game physics is a common but limited design element in computer games. It was discovered that the entertainment driven design goals of game developers interfere with the needs of educators and scientists. Game reviews exemplified the exaggerated and incorrect physics present in many commercial computer games. This “pseudo physics” was shown to have potentially undesired effects on game players. Art reviews also indicated that game physics technology remains largely inaccessible to artists. The principal conclusion drawn from this study was that the proposed new game physics advances game design and creates value by expanding the choices available to game developers and designers, enabling artists to create more scientifically robust artworks, and encouraging scientists to consider games as a viable tool for education and research. The practical portion generated tangible evidence that the isolated “silos” of engineering, art and science can be bridged when game physics is designed in a transdisciplinary way. This dissertation recommends that scientific and artistic perspectives should always be considered when game physics is used in computer-based media, because significant value for a broad range of practitioners in succinctly different fields can be achieved. The study has thereby established a state of the art research into game physics, which not only offers other researchers constructive principles for future investigations, but also provides much-needed new material to address the observed discrepancies in game theory and digital media design

Graphical modelling of modular machines

This research is aimed at advancing machine design through specifying and implementing (in "proof of concept" form) a set of tools which graphically model modular machines. The tools allow mechanical building elements (or machine modules) to be selected and configured together in a highly flexible manner so that operation of the chosen configuration can be simulated and performance properties evaluated. Implementation of the tools has involved an extension in capability of a proprietary robot simulation system. This research has resulted in a general approach to graphically modelling manufacturing machines built from modular elements. A focus of study has been on a decomposition of machine functionality leading to the establishment of a library of modular machine primitives. This provides a useful source of commonly required machine building elements for use by machine designers. Study has also focussed on the generation of machine configuration tools which facilitate the construction of a simulation model and ultimately the physical machine itself. Simulation aspects of machine control are also considered which depict methods of manipulating a machine model in the simulation phase. In addition methods of achieving machine programming have been considered which specify the machine and its operational tasks. Means of adopting common information data structures are also considered which can facilitate interfacing with other systems, including the physical machine system constructed as an issue of the simulation phase. Each of these study areas is addressed in its own context, but collectively they provide a means of creating a complete modular machine design environment which can provide significant assistance to machine designers. Part of the methodology employed in the study is based on the use of the discrete event simulation technique. To easily and effectively describe a modular machine and its activity in a simulation model, a hierarchical ring and tree data structure has been designed and implemented. The modularity and reconfigurability are accommodated by the data structure, and homogeneous transformations are adopted to determine the spatial location and orientation of each of the machine elements. A three-level machine task programming approach is used to describe the machine's activities. A common data format method is used to interface the machine design environment with the physical machine and other building blocks of manufacturing systems (such as CAD systems) where systems integration approaches can lead to enhanced product realisation. The study concludes that a modular machine design environment can be created by employing the graphical simulation approach together with a set of comprehensive configuration. tools. A generic framework has been derived which outlines the way in which machine design environments can be constructed and suggestions are made as to how the proof of concept design environment implemented in this study can be advanced

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming