Computational Models of Scientific Discovery and Theory Formation (Morgan Kaufman Series in Machine Learning)

Scientific discovery has long fascinated both philosophers and historians, but only in the past few decades have the tools become available that enable computers to model this complex process. This collection reports on recent advances in the study of scientific discovery and theory formation based on the computational techniques of artificial intelligence and cognitive science. As the chapters in this book demonstrate, the last few years have seen the work on this topic expand dramatically from a few isolated efforts into a major enterprise, involving researchers from many disciplines and focusing on many aspects of scientific behavior. The contributors to this volume come from a variety of paradigms, including artificial intelligence, cognitive psychology, and the philosophy and history of science. The topics studied also range widely, including the discovery of empirical laws, the formation and revision of theories, the design of experiments, and and the evaluation of competing hypotheses. Despite this diversity, both researchers and approaches are united in their goal of developing and understanding computational mechanisms that demonstrate scientific behavior. Many of the chapters focus on historical examples from the fields of physics, chemistry, geology, and biology, giving enlightening accounts of discovery in these domains. The chapters in this volume provide convincing evidence that the techniques of AI and cognitive science can produce coherent models of complex scientific behavior.