Learning in Humans and Machines

• Chapter headings: Towards an Interdisciplinary Learning Science (P. Reimann, H. Spada). A Cognitive Psychological Approach to Learning (S. Vosniadou). Learning to Do and Learning to Understand: A Lesson and a Challenge for Cognitive Modeling (S. Ohlsson). Machine Learning: Case Studies of an Interdisciplinary Approach (W. Emde). Mental and Physical Artifacts in Cognitive Practices (R. Saljo). Learning Theory and Instructional Science (E. De Corte). Knowledge Representation Changes in Humans and Machines (L. Saitta and Task Force 1). Multi-Objective Learning with Multiple Representations (M. Van Someren, P. Reimann). Order Effects in Incremental Learning (P. Langley). Situated Learning and Transfer (H. Gruber et al.). The Evolution of Research on Collaborative Learning (P. Dillenbourg et al.). A Developmental Case Study on Sequential Learning: The Day-Night Cycle (K. Morik, S. Vosniadou). Subject index. Author index.

Ephraim Nissan, University of Greenwich The title of this book accurately describes its editors' ambition: outstretching both arms wide open to get hold of as diverse foci as learning in humans, versus what the discipline of machine learning (ML) within artificial intelligence (AI) actually amounts to in the main...Used properly...this volume can be a trove. A trove of leads to lead you outside the grasp of its compass. To the extent that the book can do that for the reader, it has fulfilled its purpose. No other single book, to my knowledge, would do the same for us on this global subject. Pragmatics & Cognition A certain unity (in this publication's) approach, focusing on the analysis of phenomena in their compexity and developing a "flexible" vision of learning, integrating the role of context, goals and previous knowledge, gives an undeniable coherence to this work. L'Annee Psychologique