This book is designed to identify some of the current applications and techniques of artificial intelligence as an aid to solving problems and accomplishing tasks. It provides a general introduction to the various branches of AI which include formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic, etc. The book has been structured into five parts with an emphasis on expert systems: problems and state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog. Features: Introduces the various branches of AI which include formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic, etc. Includes a separate chapter on Prolog to introduce basic programming techniques in AI

This book deals with large-scale or macro-level instructional design, which is referred to by other authors variously as curriculum development, course design, training system design or instructional systems design. The emphasis throughout the book is on the application of a systems approach, which implies both a way of thinking about the problem and a methodology for seeking and developing solutions. Thus the approach of the book is problem-oriented. The successful problem-solver requires more than a technique or procedure. He requires experience of similar problems, some general principles that he can apply to the class of problems and a great deal of creativity to develop an optimal method of solving each problem. This book brings together the theories and practical experience that have been built up by instructional technologists over the last two decades, the techniques that are currently most used for the analysis of problems in education and for their solution, and a range of new ideas specially developed by the author to encourage the creative element (so often missing from educational materials). This book is intended for anyone involved in instructional design. It is designed on a ‘grid’ structure to facilitate the reader’s choice of chapters. Those who wish to gain a general overview may concentrate on the chapters at the theory base and analysis levels. Those more practically concerned with course design will find much of use in the synthesis and evaluation levels. Those who wish simply to discover ‘what’s new’ in this book and its treatment of instructional design will find what they are seeking principally in the analysis and evaluation levels.

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]

"This book seeks to examine the efforts made to bridge the gap between student and educator with computer applications through an in-depth discussion of applications employed to overcome the problems encountered during educational processes"--Provided by publisher.

Before the integration of expert systems in biomedical science, complex problems required human expertise to solve them through conventional procedural methods. Advancements in expert systems allow for knowledge to be extracted when no human expertise is available and increases productivity through quick diagnosis. Expert System Techniques in Biomedical Science Practice is an essential scholarly resource that contains innovative research on the methods by which an expert system is designed to solve complex problems through the automation of decision making through the use of if-then-else rules rather than conventional procedural methods. Featuring coverage on a broad range of topics such as image processing, bio-signals, and cognitive AI, this book is a vital reference source for computer engineers, information technologists, biomedical engineers, data-processing specialists, medical professionals, and industrialists within the fields of biomedical engineering, pervasive computing, and natural language processing.