Simulators are becoming standard equipment for interactive learning environments. They allow for attractive teaching with a large degree of freedom for the learner. However, without proper guidance, the learner easily gets lost in a simulation environment. Providing guidance requires an image of what the learner is doing. Acquiring this image by diagnosing the behaviour of the learner is a complex and resource-intensive task for which yet no general approach exists. In this book, we apply existing ideas and techniques from the field of model-based reasoning and diagnosis to interactive learning environments. We present a framework for subject matter modelling and diagnosis of learner behaviour. The framework defines generic techniques for automatically generating subject matter models from qualitative simulations. A generic model-based engine employs these models for diagnosing the learner's behaviour. The framework provides a powerful and reusable approach to individualising guidance in educational systems.
The volume is based on the papers that were presented at the Interna tional Conference Model-Based Reasoning in Scientific Discovery (MBR'98), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 1998. The papers explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The study of diagnostic, visual, spatial, analogical, and temporal rea soning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of tradi tional notions of reasoning such as classical logic. Traditional accounts of scientific reasoning have restricted the notion of reasoning primarily to de ductive and inductive arguments. Understanding the contribution of model ing practices to discovery and conceptual change in science requires ex panding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philoso phy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model based reasoning to be considered in this book. The models are intended as in terpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain.
The field of Artificial Intelligence in Education includes research and researchers from many areas of technology and social science. This study aims to open opportunities for the cross-fertilization of information and ideas from researchers in the many fields that make up this interdisciplinary research area.
This book constitutes the refereed proceedings of the 15th International Conference on Artificial Intelligence in Education, AIED 2011, held in Auckland, New Zealand in June/July 2011. The 49 revised full papers presented together with three invited talks and extended abstracts of poster presentations, young researchers contributions and interactive systems reports and workshop reports were carefully reviewed and selected from a total of 193 submissions. The papers report on technical advances in and cross-fertilization of approaches and ideas from the many topical areas that make up this highly interdisciplinary field of research and development including artificial intelligence, agent technology, computer science, cognitive and learning sciences, education, educational technology, game design, psychology, philosophy, sociology, anthropology and linguistics.
This book constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence in Education, AIED 2015, held in Madrid, Spain, in June 2015. The 50 revised full papers presented together with 3 keynotes, 79 poster presentations, 13 doctoral consortium papers, 16 workshop abstracts, and 8 interactive event papers were carefully reviewed and selected from numerous submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics, as well as many domain-specific areas.
The theme of TechEd2000 was Teaching and Learning in a Network World. The conference brings together professionals from all sectors and levels of education, and provides a vision of the exciting, interactive, technology-rich future of education. TechEd2000 plays a vital role in equipping universities, colleges, and schools with a strong base technology awareness and the knowledge needed to tackle the increasing challenges of teaching and learning. Presentations accepted for TechEd2000 demonstrate innovative solutions for key technology issues faced by educational institutions from throughout the world.
The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition features: A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com. Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.
The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
The annual Kes International Conference in Knowledge-based Intelligent Information Engineering Systems and Allied Technologies has become an event that is held in high regard by the intelligent systems community. The proceedings of the fifth conference represents a comprehensive survey of research on the theory and application of knowledge-based intelligent systems including topics such as: generic intelligent techniques - artificial neural networks, machine learning fuzzy and neuro-fuzzy techniques, and artificial life; applications of intelligent systems - condition monitoring, fault diagnosis, image processing, and high voltage systems; and allied technologies - communications, the Internet and web-based technologies, e-commerce, and computer pets. The proceedings should be of interest to those in the intelligent systems field, such as engineers, researchers and students.
