After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems.

This book provides a comprehensive, up-to-date look at problem solving research and practice over the last fifteen years. The first chapter describes differences in types of problems, individual differences among problem-solvers, as well as the domain and context within which a problem is being solved. Part one describes six kinds of problems and the methods required to solve them. Part two goes beyond traditional discussions of case design and introduces six different purposes or functions of cases, the building blocks of problem-solving learning environments. It also describes methods for constructing cases to support problem solving. Part three introduces a number of cognitive skills required for studying cases and solving problems. Finally, Part four describes several methods for assessing problem solving. Key features includes: Teaching Focus – The book is not merely a review of research. It also provides specific research-based advice on how to design problem-solving learning environments. Illustrative Cases – A rich array of cases illustrates how to build problem-solving learning environments. Part two introduces six different functions of cases and also describes the parameters of a case. Chapter Integration – Key theories and concepts are addressed across chapters and links to other chapters are made explicit. The idea is to show how different kinds of problems, cases, skills, and assessments are integrated. Author expertise – A prolific researcher and writer, the author has been researching and publishing books and articles on learning to solve problems for the past fifteen years. This book is appropriate for advanced courses in instructional design and technology, science education, applied cognitive psychology, thinking and reasoning, and educational psychology. Instructional designers, especially those involved in designing problem-based learning, as well as curriculum designers who seek new ways of structuring curriculum will find it an invaluable reference tool.

From executives complaining that their teams don’t contribute ideas to employees giving up because their input isn’t valued--company culture is the culprit. Courageous Cultures provides a road map to build a high-performance, high-engagement culture around sharing ideas, solving problems, and rewarding contributions from all levels. Many leaders are convinced they have an open environment that encourages employees to speak up and are shocked when they learn that employees are holding back. Employees have ideas and want to be heard. Leadership wants to hear them. Too often, however, employees and leaders both feel that no one cares about making things better. The disconnect typically only widens over time, with both sides becoming more firmly entrenched in their viewpoints. Becoming a courageous culture means building teams of microinnovators, problem solvers, and customer advocates working together. In our world of rapid change, a courageous culture is your competitive advantage. It ensures that your company is “sticky” for both customers and employees. In Courageous Cultures, you’ll learn practical tools that help you: Learn the difference between microinnovators, problem solvers, and customer advocates and how they work together. See how the latest research conducted by the authors confirms why organizations struggle when it comes to creating strong cultures where employees are encouraged to contribute their best thinking. Learn proven models and tools that leaders can apply throughout all levels of the organization, to reengage and motivate employees. Understand best practices from companies around the world and learn how to apply these strategies and techniques in your own organization. This book provides you with the practical tools to uncover, leverage, and scale the best ideas from every level of your organization.

This book is the first to systematically describe the key components necessary to ensure successful implementation of Collaborative Problem Solving (CPS) across mental health settings and non-mental health settings that require behavioral management. This resource is designed by the leading experts in CPS and is focused on the clinical and implementation strategies that have proved most successful within various private and institutional agencies. The book begins by defining the approach before delving into the neurobiological components that are key to understanding this concept. Next, the book covers the best practices for implementation and evaluating outcomes, both in the long and short term. The book concludes with a summary of the concept and recommendations for additional resources, making it an excellent concise guide to this cutting edge approach. Collaborative Problem Solving is an excellent resource for psychiatrists, psychologists, social workers, and all medical professionals working to manage troubling behaviors. The text is also valuable for readers interested in public health, education, improved law enforcement strategies, and all stakeholders seeking to implement this approach within their program, organization, and/or system of care.

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Most artificial intelligence research investigates intelligent behavior for a single agent--solving problems heuristically, understanding natural language, and so on. Distributed Artificial Intelligence (DAI) is concerned with coordinated intelligent behavior: intelligent agents coordinating their knowledge, skills, and plans to act or solve problems, working toward a single goal, or toward separate, individual goals that interact. DAI provides intellectual insights about organization, interaction, and problem solving among intelligent agents. This comprehensive collection of articles shows the breadth and depth of DAI research. The selected information is relevant to emerging DAI technologies as well as to practical problems in artificial intelligence, distributed computing systems, and human-computer interaction. "Readings in Distributed Artificial Intelligence" proposes a framework for understanding the problems and possibilities of DAI. It divides the study into three realms: the natural systems approach (emulating strategies and representations people use to coordinate their activities), the engineering/science perspective (building automated, coordinated problem solvers for specific applications), and a third, hybrid approach that is useful in analyzing and developing mixed collections of machines and human agents working together. The editors introduce the volume with an important survey of the motivations, research, and results of work in DAI. This historical and conceptual overview combines with chapter introductions to guide the reader through this fascinating field. A unique and extensive bibliography is also provided.

Complex problem solving is the core skill for 21st Century Teams Complex problem solving is at the very top of the list of essential skills for career progression in the modern world. But how problem solving is taught in our schools, universities, businesses and organizations comes up short. In Bulletproof Problem Solving: The One Skill That Changes Everything you’ll learn the seven-step systematic approach to creative problem solving developed in top consulting firms that will work in any field or industry, turning you into a highly sought-after bulletproof problem solver who can tackle challenges that others balk at. The problem-solving technique outlined in this book is based on a highly visual, logic-tree method that can be applied to everything from everyday decisions to strategic issues in business to global social challenges. The authors, with decades of experience at McKinsey and Company, provide 30 detailed, real-world examples, so you can see exactly how the technique works in action. With this bulletproof approach to defining, unpacking, understanding, and ultimately solving problems, you’ll have a personal superpower for developing compelling solutions in your workplace. Discover the time-tested 7-step technique to problem solving that top consulting professionals employ Learn how a simple visual system can help you break down and understand the component parts of even the most complex problems Build team brainstorming techniques that fight cognitive bias, streamline workplanning, and speed solutions Know when and how to employ modern analytic tools and techniques from machine learning to game theory Learn how to structure and communicate your findings to convince audiences and compel action The secrets revealed in Bulletproof Problem Solving will transform the way you approach problems and take you to the next level of business and personal success.