https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

Interpretability aspects of fuzzy models have received quite some attention in recent years and may be obtained by using transparent rule-structures and well characterized fuzzy membership functions. Moreover, model compactness is important for the interpretability and is related to the number of rules and fuzzy sets. Besides these two criteria, the model accuracy should always be taken into account. In this way, several criteria appear in fuzzy modeling and then multi-objective evolutionary algorithms are suitable to capture several nondominated solutions in a single run of the algorithm. For fuzzy modeling, we describe a multi-objective neuro-evolutionary algorithm that considers all three objectives. The algorithm applies an accuracy criterium and a transparency criterium, based on fuzzy set similarity, while compactness is achieved by a specific technique, incorporated ad hoc within the evolutionary algorithm. Results are shown for an approximation problem studied before by other authors.

Fuzzy modeling with multi-objective neuro-evolutionary algorithms

http://busoniu.net/files/papers/ifac08-elevators.pdf

Reinforcement Learning for Elevator Control

The state-of-the-art methodology for the design of digital flight-by-wire flight control laws is based on dimming and linearization of a nonlinear aircraft model at selected operating points and subsequent tuning of linear control laws. Despite recent advances in the development of computer-aided control design toots, selection of the operating points and the design of the gain schedule still has to be done manually, in a heuristic manner. In order to reduce the design effort, an automated procedure has been developed. The number of operating points and their locations are determined automatically on the basis of the changes in the aerodynamics over the flight envelope, by using fuzzy clustering. This approach also directly provides the interpolation mechanism (membership functions) for the local control law parameters. The procedure has been developed in close cooperation with airframe and control system manufacturers and was evaluated in pilot-in-the-loop flight simulator tests.

Fuzzy gain scheduling for flight control laws

We present an unscented Kalman filter based state estimator for a fast moving rigid body (such as a mobile robot) endowed with two video cameras. We focus on forward velocity estimation towards the computation of standard energy cost functions for legged locomotion. Points are chosen as image features and the model of each camera is based on the traditional pinhole projection. The resulting filter's state is composed of the rigid body pose and velocities, together with a measure of depth for each tracked point. By taking inspiration from nature's large predatory and grazing mammals eye configuration, we suggest, via simulation results, a solution for the question of finding the best orientation of two cameras, between side and frontal facing, for velocity estimation in a forward moving robot.

Motion estimation based on predator/prey vision

Hopper dredgers are massive ships that excavate sediments from the sea bottom while sailing. The excavated material is then transported and discharged at a specified location. The efficiency of this process is highly dependent on the detailed knowledge of the excavated soil. When the soil is composed mainly of sand, the parameter of the greatest importance is the average grain diameter. This, however cannot be directly measured by available sensors. Therefore, in this paper a particle filter is proposed to estimate the average grain diameter. The estimation is based on online measurements of the total height of the mixture in the hopper, total mass, the incoming mixture density and flow-rate and the height of a sand bed, together with estimates of the outgoing mixture density and flow-rate. The loading process is naturally decomposed into three phases and the filter is applied to the first two phases. In order to match different types of nonlinearities, a separate observer is proposed for each phase under consideration. This increases the modularity of the filter and makes tuning easier. The performance of the filter is evaluated in simulations and the results are encouraging.

Robert Babuska

Papers

Fuzzy modeling with multi-objective neuro-evolutionary algorithms

Reinforcement Learning for Elevator Control

Fuzzy gain scheduling for flight control laws

Motion estimation based on predator/prey vision

Particle filters for estimating average grain diameter of material excavated by hopper dredger