Reports on industrial information technology. Vol. 12

The 12th volume of Reports on Industrial Information Technology presents some selected results of research achieved at the Institute of Industrial Information Technology during the last two years.These results have contributed to many cooperative projects with partners from academia and industry and cover current research interests including signal and image processing, pattern recognition, distributed systems, powerline communications, automotive applications, and robotics

Motion Strategies for Visibility based Target Tracking in Unknown Environments

Ph.DDOCTOR OF PHILOSOPH

Reinforcement learning in large state action spaces

Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

Application of robust nonlinear model predictive control to simulating the control behaviour of a racing driver

The work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory

What does explainable AI explain?

Machine Learning (ML) models are increasingly used in industry, as well as in scientific research and social contexts. Unfortunately, ML models provide only partial solutions to real-world problems, focusing on predictive performance in static environments. Problem aspects beyond prediction, such as robustness in employment, knowledge generation in science, or providing recourse recommendations to end-users, cannot be directly tackled with ML models. Explainable Artificial Intelligence (XAI) aims to solve, or at least highlight, problem aspects beyond predictive performance through explanations. However, the field is still in its infancy, as fundamental questions such as “What are explanations?”, “What constitutes a good explanation?”, or “How relate explanation and understanding?” remain open. In this dissertation, I combine philosophical conceptual analysis and mathematical formalization to clarify a prerequisite of these difficult questions, namely what XAI explains: I point out that XAI explanations are either associative or causal and either aim to explain the ML model or the modeled phenomenon. The thesis is a collection of five individual research papers that all aim to clarify how different problems in XAI are related to these different “whats”. In Paper I, my co-authors and I illustrate how to construct XAI methods for inferring associational phenomenon relationships. Paper II directly relates to the first; we formally show how to quantify uncertainty of such scientific inferences for two XAI methods – partial dependence plots (PDP) and permutation feature importance (PFI). Paper III discusses the relationship between counterfactual explanations and adversarial examples; I argue that adversarial examples can be described as counterfactual explanations that alter the prediction but not the underlying target variable. In Paper IV, my co-authors and I argue that algorithmic recourse recommendations should help data-subjects improve their qualification rather than to game the predictor. In Paper V, we address general problems with model agnostic XAI methods and identify possible solutions

Irish Ocean Climate and Ecosystem Status Report

Summary report for Irish Ocean Climate & Ecosystem Status Report also published here. This Irish Ocean Climate & Ecosystem Status Summary for Policymakers brings together the latest evidence of ocean change in Irish waters. The report is intended to summarise the current trends in atmospheric patterns, ocean warming, sea level rise, ocean acidification, plankton and fish distributions and abundance, and seabird population trends. The report represents a collaboration between marine researchers within the Marine Institute and others based in Ireland’s higher education institutes and public bodies. It includes authors from Met Éireann, Maynooth University, the University of Galway, the Atlantic Technological University, National Parks and Wildlife, Birdwatch Ireland, Trinity College Dublin, University College Dublin, Inland Fisheries Ireland, The National Water Forum, the Environmental Protection Agency, and the Dundalk Institute of Technology.This report is intended to summarise the current trends in Ireland’s ocean climate. Use has been made of archived marine data held by a range of organisations to elucidate some of the key trends observed in phenomena such as atmospheric changes, ocean warming, sea level rise, acidification, plankton and fish distributions and abundance, and seabirds. The report aims to summarise the key findings and recommendations in each of these areas as a guide to climate adaptation policy and for the public. It builds on the previous Ocean Climate & Ecosystem Status Report published in 2010. The report examines the recently published literature in each of the topic areas and combines this in many cases with analysis of new data sets including long-term time series to identify trends in essential ocean variables in Irish waters. In some cases, model projections of the likely future state of the atmosphere and ocean are presented under different climate emission scenarios.Marine Institut

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Transformation Literacy

This open access book brings science and practice together and inspires a global movement towards co-creating regenerative civilizations that work for 100% of humanity and the Earth as a whole. With its conceptual foundation of the concept of transformation literacy it enhances the knowledge and capacity of decision-makers, change agents and institutional actors to steward transformations effectively across institutions, societal sectors and nations. Humanity is at crossroads. Resource depletion and exponential emissions that not only cause climate change, but endanger the health of people and planet, call for a decisive turnaround of human civilization. A new and transformative paradigm is emerging that advocates for regenerative civilizations, in which a narrative of systemic health as much as individual and collective vitality guide the interaction of socio-economic-ecological systems. Truly transformative change must go far beyond technical solutions, and instead envision what can be termed ‘a new operating system’ that helps humankind to live well within the planetary boundaries and partner with life’s evolutionary processes. This requires transformations at three different levels: · Mindsets that reconnect with a worldview in which human agency acknowledges its co-evolutionary pathways with each other and the Earth. · Political, social and economic systems that are regenerative and foster the care-taking for Earth life support systems. · Competencies to design and implement effective large-scale transformative change processes at multiple levels with multiple stakeholders. This book provides key ingredients for enhancing transformation literacy from various perspectives around the globe. It connects the emerging practice of stewarding transformative change across business, government institutions and civil society actors with the most promising scientific models and concepts that underpin human action to shape the future collectively in accordance with planetary needs.

Transformation Literacy

This open access book brings science and practice together and inspires a global movement towards co-creating regenerative civilizations that work for 100% of humanity and the Earth as a whole. With its conceptual foundation of the concept of transformation literacy it enhances the knowledge and capacity of decision-makers, change agents and institutional actors to steward transformations effectively across institutions, societal sectors and nations. Humanity is at crossroads. Resource depletion and exponential emissions that not only cause climate change, but endanger the health of people and planet, call for a decisive turnaround of human civilization. A new and transformative paradigm is emerging that advocates for regenerative civilizations, in which a narrative of systemic health as much as individual and collective vitality guide the interaction of socio-economic-ecological systems. Truly transformative change must go far beyond technical solutions, and instead envision what can be termed ‘a new operating system’ that helps humankind to live well within the planetary boundaries and partner with life’s evolutionary processes. This requires transformations at three different levels: · Mindsets that reconnect with a worldview in which human agency acknowledges its co-evolutionary pathways with each other and the Earth. · Political, social and economic systems that are regenerative and foster the care-taking for Earth life support systems. · Competencies to design and implement effective large-scale transformative change processes at multiple levels with multiple stakeholders. This book provides key ingredients for enhancing transformation literacy from various perspectives around the globe. It connects the emerging practice of stewarding transformative change across business, government institutions and civil society actors with the most promising scientific models and concepts that underpin human action to shape the future collectively in accordance with planetary needs.

Linkages between stratospheric ozone, UV radiation and climate change and their implications for terrestrial ecosystems

Exposure of plants and animals to ultraviolet-B radiation (UV-B; 280-315 nm) is modified by stratospheric ozone dynamics and climate change. Even though stabilisation and projected recovery of stratospheric ozone is expected to curtail future increases in UV-B radiation at the Earth’s surface, on-going changes in climate are increasingly exposing plants and animals to novel combinations of UV-B radiation and other climate change factors (e.g., ultraviolet-A and visible radiation, water availability, temperature and elevated carbon dioxide). Climate change is also shifting vegetation cover, geographic ranges of species, and seasonal timing of development, which further modifies exposure to UV-B radiation. Since our last assessment, there is increased understanding of the underlying mechanisms by which plants perceive UV-B radiation, eliciting changes in growth, development and tolerances of abiotic and biotic factors. However, major questions remain on how UV-B radiation is interacting with other climate change factors to modify the production and quality of crops, as well as important ecosystem processes such as plant and animal competition, pest-pathogen interactions, and the decomposition of dead plant matter (litter). In addition, stratospheric ozone depletion is directly contributing to climate change in the southern hemisphere, such that terrestrial ecosystems in this region are being exposed to altered patterns of precipitation, temperature and fire regimes as well as UV-B radiation. These ozone-driven changes in climate have been implicated in both increases and reductions in the growth, survival and reproduction of plants and animals in Antarctica, South America and New Zealand. In this assessment, we summarise advances in our knowledge of these and other linkages and effects, and identify uncertainties and knowledge gaps that limit our ability to fully evaluate the ecological consequences of these environmental changes on terrestrial ecosystems.Peer reviewe