Kenneth D. Forbus

Kenneth D. Forbus is Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. He is the coauthor of Building Problem Solvers and the coeditor of Smart Machines in Education, both published by the MIT Press.

  • Qualitative Representations

    How People Reason and Learn about the Continuous World

    Kenneth D. Forbus

    An argument that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition.

    In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representations a useful component of natural language semantics. Qualitative representations also provide a foundation for expert reasoning in science and engineering by making explicit the broad categories of things that might happen and enabling causal models that help guide the application of more quantitative knowledge as needed. Qualitative representations are important for creating more human-like artificial intelligence systems with capabilities for spatial reasoning, vision, question answering, and understanding natural language.

    Forbus discusses, among other topics, basic ideas of knowledge representation and reasoning; qualitative process theory; qualitative simulation and reasoning about change; compositional modeling; qualitative spatial reasoning; and learning and conceptual change. His argument is notable both for presenting an approach to qualitative reasoning in which analogical reasoning and learning play crucial roles and for marshaling a wide variety of evidence, including the performance of AI systems. Cognitive scientists will find Forbus's account of qualitative representations illuminating; AI scientists will value Forbus's new approach to qualitative representations and the overview he offers.

    • Hardcover $50.00
  • Smart Machines in Education

    Smart Machines in Education

    Kenneth D. Forbus and Paul J. Feltovich

    The emerging widespread use of artificial intelligence in education.

    Multimedia, simulation, computer-mediated communication networks, and distance learning have all become part of the educational toolkit. The next major technology to change the face of education will be based on the widespread use of artificial intelligence (AI). Progress in AI has led to a deeper understanding of how to represent knowledge, to reason, and to describe procedural knowledge. Progress in cognitive science has led to a deeper understanding of how people think, solve problems, and learn. AI scientists use results from cognitive science to create software with more humanlike abilities, which can help students learn better. This book looks at some of the results of this synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies.

  • Building Problem Solvers

    Building Problem Solvers

    Kenneth D. Forbus and Johan De Kleer

    For nearly two decades, Kenneth Forbus and Johan de Kleer have accumulated a substantial body of knowledge about the principles and practice of creating problem solvers. In some cases they are the inventors of the ideas or techniques described, and in others, participants in their development.

    Building Problem Solvers communicates this knowledge in a focused, cohesive manner. It is unique among standard artificial intelligence texts in combining science and engineering, theory and craft to describe the construction of AI reasoning systems, and it includes code illustrating the ideas.

    After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems. The diligent reader will have worked through several substantial examples, including systems that perform symbolic algebra, natural deduction, resolution, qualitative reasoning, planning, diagnosis, scene analysis, and temporal reasoning.

    • Hardcover $90.00
    • Paperback $92.00