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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

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

https://science.sciencemag.org/content/sci/5/110/206.full.pdf

The american psychological association.

https://cyberleninka.org/article/n/140802.pdf

Gamification for Health and Wellbeing : A Systematic Review of the Literature

An approach to robotic path planning, which allows optimization of useful performance indices in the presence of obstacles, is given. The main idea is to express obstacle avoidance in terms of the distances between potentially colliding parts. Mathematical properties of the distance functions are studied and it is seen that various types of derivatives of the distance functions are easily characterized. The results lead to the formulation of path planning problems as problems in optimal control and suggest numerical procedures for their solution. A simple numerical example involving a three-degree-of-freedom Cartesian manipulator is described.

/pdf/distance-functions-and-their-application-to-robot-path-26y49ifg42.pdf

Distance functions and their application to robot path planning in the presence of obstacles

We report the results of a short programme of mindfulness training administered to adolescent boys in a classroom setting. Intervention and control groups (N = 155) were compared on measures of mindfulness, resilience and psychological well-being. Although the overall differences between the two groups failed to reach significance, we found that within the mindfulness group, there was a significant positive association between the amount of individual practice outside the classroom and improvement in psychological well-being and mindfulness. We also found that the improvement in well-being was related to personality variables (agreeableness and emotional stability). Most students reported enjoying and benefiting from the mindfulness training, and 74% said they would like to continue with it in the future. The results of this preliminary study are encouraging. Further work is needed to refine the training programme and undertake a definitive randomised controlled trial, using both subjective and objective ...

/pdf/a-controlled-trial-of-mindfulness-training-in-schools-the-3z13cqeo94.pdf

A controlled trial of mindfulness training in schools: The importance of practice for an impact on well-being

It is proposed that games, which are designed to generate positive affect, are most successful when they facilitate flow (Csikszentmihalyi 1992). Flow is a state of concentration, deep enjoyment, and total absorption in an activity. The study of games, and a resulting understanding of flow in games can inform the design of non-leisure software for positive affect. The paper considers the ways in which computer games contravene Nielsen's guidelines for heuristic evaluation (Nielsen and Molich 1990) and how these contraventions impact on flow. The paper also explores the implications for research that stem from the differences between games played on a personal computer and games played on a dedicated console. This research takes important initial steps towards defining how flow in computer games can inform affective design.

Effective affective user interface design in games

/pdf/effective-affective-user-interface-design-in-games-1i56jxnal9.pdf

Daniel Johnson

Papers

Gamification for Health and Wellbeing : A Systematic Review of the Literature

Distance functions and their application to robot path planning in the presence of obstacles

A controlled trial of mindfulness training in schools: The importance of practice for an impact on well-being

Effective affective user interface design in games

Effective affective user interface design in games