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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

An Introduction to MultiAgent Systems.

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.

Autonomous vehicle perception: The technology of today and tomorrow

Display of stereo images is widely used to enhance the viewing experience of three-dimensional imaging and
communication systems. In this paper, we propose a method for estimating the quality of stereoscopic images
using segmented image features and disparity. This method is inspired by the human visual system. We believe
the perceived distortion and disparity of any stereoscopic display is strongly dependent on local features, such
as edge (non-plane) and non-edge (plane) areas. Therefore, a no-reference perceptual quality assessment is
developed for JPEG coded stereoscopic images based on segmented local features of artifacts and disparity.
Local feature information such as edge and non-edge area based relative disparity estimation, as well as the
blockiness and the blur within the block of images are evaluated in this method. Two subjective stereo image
databases are used to evaluate the performance of our method. The subjective experiments results indicate our
model has sufficient prediction performance.

No-reference stereoscopic image quality assessment

We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Coordinated Action in a Heterogeneous Rescue Team.- Concept Evaluation of a Reflex Inspired Ball Handling Device for Autonomous Soccer Robots.- Development of a Realistic Simulator for Robotic Intelligent Wheelchairs in a Hospital Environment.- Creating Photo Maps with an Aerial Vehicle in USARsim.- Real-Time Hand Gesture Recognition for Human Robot Interaction.- Combining Key Frame Based Motion Design with Controlled Movement Execution.- Applying Dynamic Walking Control for Biped Robots.- Modeling Human Decision Making Using Extended Behavior Networks.- Motion Synthesis through Randomized Exploration on Submanifolds of Configuration Space.- Robust and Computationally Efficient Navigation in Domestic Environments.- Robust Collision Avoidance in Unknown Domestic Environments.- Real-Time Ball Tracking in a Semi-automated Foosball Table.- Three Humanoid Soccer Platforms: Comparison and Synthesis.- Learning Complementary Multiagent Behaviors: A Case Study.- Rollover as a Gait in Legged Autonomous Robots: A Systems Analysis.- Pareto-Optimal Collaborative Defensive Player Positioning in Simulated Soccer.- A Novel Camera Parameters Auto-adjusting Method Based on Image Entropy.- Object Recognition with Statistically Independent Features: A Model Inspired by the Primate Visual Cortex.- Using Genetic Algorithms for Real-Time Object Detection.- An Approximate Computation of the Dominant Region Diagram for the Real-Time Analysis of Group Behaviors.- A Lua-based Behavior Engine for Controlling the Humanoid Robot Nao.- Stable Mapping Using a Hyper Particle Filter.- A Characterization of 3D Sensors for Response Robots.- Multiple Model Kalman Filters: A Localization Technique for RoboCup Soccer.- Integrated Genetic Algorithmic and Fuzzy Logic Approach for Decision Making of Police Force Agents in Rescue Simulation Environment.- IntellWheels MMI: A Flexible Interface for an Intelligent Wheelchair.- Analyzing the Human-Robot Interaction Abilities of a General-Purpose Social Robot in Different Naturalistic Environments.- Communicating among Robots in the RoboCup Middle-Size League.- Multi-robot Cooperative Object Localization.- Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization.- Efficient Behavior Learning by Utilizing Estimated State Value of Self and Teammates.- Sensor and Information Fusion Applied to a Robotic Soccer Team.- Omnidirectional Walking Using ZMP and Preview Control for the NAO Humanoid Robot.- RoboCup@Home: Results in Benchmarking Domestic Service Robots.- Connecting the Real World with the Virtual World - Controlling AIBO through Second Life.- A Hybrid Agent Simulation System of Rescue Simulation and USARSim Simulations from Going to Fire-Escape Doors to Evacuation to Shelters.- SSL-Vision: The Shared Vision System for the RoboCup Small Size League.- Heuristic Formation Control in Multi-robot Systems Using Local Communication and Limited Identification.- Cooperative Multi-robot Map Merging Using Fast-SLAM.

RoboCup 2009: Robot Soccer world cup XIII

This article describes the history and major achievements of the RoboCup Humanoid League from its start in 2002 to today. Furthermore, it gives an indication on how the league may evolve over the coming years until 2050, when a team of autonomous humanoid robots shall play soccer against the human world champion. We show how the competition drives humanoid robot research and serves as a benchmark to measure progress.

Jacky Baltes

Papers

No-reference stereoscopic image quality assessment

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

RoboCup 2009: Robot Soccer world cup XIII

Humanoid Robots in Soccer: Robots Versus Humans in RoboCup 2050

The IROS 2016 Competitions [Competitions]