Model-Based Reasoning in Scientific Discovery

• Author(s): L. Magnani, N.J. Nersessian, Paul Thagard,
• Publisher: Springer Science & Business Media
• Pages: 343
• ISBN_10: 1461548136
  ISBN_13: 9781461548133
  
• Language: en
• Categories: Computers / Computer Simulation , Science / Philosophy & Social Aspects , Science / System Theory , Philosophy / Logic , Computers / Artificial Intelligence / General , Psychology / Cognitive Psychology & Cognition , Computers / Software Development & Engineering / General , Language Arts & Disciplines / Library & Information Science / General ,

Description:... 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.