자연주의적 탐구조사(Naturalistic Inquiry)

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

Dementia prevention, intervention, and care

From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

The rapidly ageing population is placing increasing strain on healthcare services. Robots have been proposed as a way to assist people to stay healthy and safe in their own homes. However, despite the need for such assistive devices and the success of some healthcare robots, other robots have had a poor response. This article reviews the literature about human responses to healthcare robots and summarises the variables that have been found to influence responses. It may be possible to increase acceptance of healthcare robots by properly assessing the needs of the human user and then matching the robot’s role, appearance and behaviour to these needs. Because robots have limitations in their abilities, another way to increase acceptance may be to modify the expectations of users to better match robots’ abilities. More research needs to investigate potential users’ needs and expectations in specific situations and whether interventions to increase the match between robot and human can increase acceptance.

Acceptance of Healthcare Robots for the Older Population: Review and Future Directions

http://www.phoque-paro.fr/wp-content/uploads/2013/11/2013-Paro-loneliness-RCT.pdf

The psychosocial effects of a companion robot: a randomized controlled trial.

This review aimed to identify the areas of need that older people have, and the available solutions. In particular, the robotic solutions are explored and critiqued and areas for future development identified. The literature was reviewed for factors that influence admission to nursing home care, and for technological solutions to these factors. The main issues facing older people are physical decline, cognitive decline, health management, and psychosocial issues. Robots exist that may meet some of the identified issues but gaps where robots could be developed include delivering interventions to prevent physical decline occurring and robots with multiple functions, including a range of cognitive stimuli and health education. To reduce barriers to acceptance, robots designed to provide physical and healthcare assistance should have a serious appearance. On the other hand animal-like robots can address psychosocial issues and function like pets. While smart phones and computers can offer some solutions, robots may promote adherence due to a social presence. Robots are being developed to address areas of need in older people, including physical, cognitive, medical and psychosocial issues. However more focus could be placed on developing preventative interventions, multifunctional robots, greater educational content and motivational aspects of appearance and interaction style.

The Role of Healthcare Robots for Older People at Home: A Review

Player/Stage has become a de facto standard in the open source robotics community. We describe recent work on restructuring the Player robot device server into a system that more closely aligns with the idea of a robot framework. The general requirements for a robot framework are also discussed. Player 2.0 is a major improvement in two basic areas, simplicity and flexibility. The driver API has been vastly simplified with many more parts of the communication being hidden from the user. The flexibility of the new system is realised in the library division that allows for different transport layers (or no transport at all), and in the new device address structure that allows for inter-server device subscriptions.

Player 2.0: Toward a Practical Robot Programming Framework

Aim: This study investigated the attitudes and preferences of staff, residents and relatives of residents in a retirement village towards a health-care robot.

Methods:  Focus groups were conducted with residents, managers and caregivers, and questionnaires were collected from 32 residents, 30 staff and 27 relatives of residents.

Results:  The most popular robot tasks were detection of falls and calling for help, lifting, and monitoring location. Robot functionality was more important than appearance. Concerns included the loss of jobs and personal care, while perceived benefits included allowing staff to spend quality time with residents, and helping residents with self-care. Residents showed a more positive attitude towards robots than both staff and relatives.

Conclusions:  These results provide an initial guide for the tasks and appearance appropriate for a robot to provide assistance in aged care facilities and highlight concerns.

Bruce A. MacDonald

Papers

Acceptance of Healthcare Robots for the Older Population: Review and Future Directions

The psychosocial effects of a companion robot: a randomized controlled trial.

The Role of Healthcare Robots for Older People at Home: A Review

Player 2.0: Toward a Practical Robot Programming Framework

Attitudes towards health-care robots in a retirement village.