SupRB: A Supervised Rule-based Learning System for Continuous Problems

We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.Comment: Submitted to the Genetic and Evolutionary Computation Conference 2020 (GECCO 2020

On the analysis and design of genetic fuzzy controllers : An application to automatic generation control of large interconnected power systems using genetic fuzzy rule based systems.

Frequency Control of large interconnected power systems is governed by means of Automatic Generation Control (AGC), which regulates the system frequency and tie line power interchange at its nominal parameter set points. Conventional approaches to AGC controller design is centered around the Proportional, Integral and Derivative (PID) controller structures, which have found widespread application within industry. However, the dynamic changes experienced throughout the life cycle of power systems have many contributing factors, in part attributed to unknown knowledge of system behavior, neglected process dynamics and a limited knowledge of system interactions, which makes modeling for AGC systems particularly trying for conventional AGC controller design approaches. Therefore, in this study, Genetic - Fuzzy controllers (GA - Fuzzy) are applied as plausible candidates for Automatic Generation Controller design and application. In GA - Fuzzy controllers, genetic algorithms which are based on the foundation of evolutionary heuristics are used as a global search method for FLC design. This is particularly motivated by the fact that Fuzzy controllers, especially where there are large data sets, unknown process knowledge and insu cient expert data available, FLC controller design proves to be a daunting task. Therefore, this thesis explores the automatic design of FLC controllers through evolutionary heuristics and applies the designed controller to the AGC problem of large interconnected power systems. The design methodology followed is to understand power system interactions through power plant modeling and the simulation power plant models for the basis for AGC controller design. It is shown in this study that the performance of the GA - Fuzzy controller have favourable characteristics in terms of robust performance, robustness properties and compares favorably with conventional AGC controller techniques. The analysis of the GA - Fuzzy controller shows that problem formulation and chromosome encoding of the problem search space forms an important prerequisite for controller design by evolutionary methods. Therefore the study concludes by stating that GA - Fuzzy controllers are plausible for application within the power industry because of its desirable attributes and that future work would include extending this research into areas of renewable energy for study and application

Deep Neural Networks and Data for Automated Driving

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties