The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of creative changes in science. The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning which cannot be described by classical logic alone. The study of these high-level methods of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic: at the heart of cognitive science. Model based reasoning promotes conceptual change because it is effective in abstracting, generating, and integrating constraints in ways that produce novel results. There are several key ingredients common to the various forms of model-based reasoning to be considered in this presentation. The models are intended as interpretations 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.In the modeling process, various forms of abstraction, such as limiting case, idealization, generalization, and generic modeling are utilized. Evaluation and adaptation take place in the light of structural of structural, causal, and/or functional constraint satisfaction and enhanced understanding of the target problem is obtained through the modeling process. Simulation can be used to produce new states and enable evaluation of behaviors, constraint satisfaction, and other factors. The book also addresses some of the main aspects of the concept of abduction, connecting it to the central epistemological question of hypothesis withdrawal in science and model-based reasoning, where abductive interferences exhibit their most appealing cognitive virtues. The most recent results and achievements in the above areas are illustrated in detail by the various contributors to the work, who are among the most respected researchers in philosophy, artificial intelligence and cognitive science.

There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term 'model' comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book's contributors are researchers active in the area of creative reasoning in science and technology.

For some time now the philosophy of science has been undergoing a major transfor mation. It began when the 'received view' of scientific knowledge -that developed by logical positivists and their intellectual descendants - was challenged as bearing little resemblance to and having little relevance for the understanding of real science. Subsequently, an overwhelming amount of criticism has been added. One would be hard-pressed to find anyone who would support the 'received view' today. Yet, in the search for a new analysis of scientific knowledge, this view continues to exert influence over the tenor of much of present-day philosophy of science; in particular, over its problems and its methods of analysis. There has, however, emerged an area within the discipline - called by some the 'new philosophy of science' - that has been engaged in transforming the problems and methods of philosophy of science. While there is far from a consensus of beliefs in this area, most of the following contentions would be affirmed by those working in it: - that science is an open-ended, on-going activity, whose character has changed significantly during its history - that science is not a monolithic enterprise - that good science can lead to false theories - that science has its roots in everyday circumstances, needs, methods, concepts, etc."

This volume is based on the papers that were presented at the International Conference "Model-Based Reasoning: Scientific Discovery, Technological Innovation, Values' (MBR'01), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in May 2001. The previous volume Model-Based Reasoning in Scientific Discovery, edited by L. Magnani, N.J. Nersessian, and P. Thagard was based on the papers presented at the first" model-based reasoning' international conference, held at the same venue in December 1998.

The presentations given at the Conference explore how scientific thinking uses models and exploratory reasoning to produce creative changes in theories and concepts. Some address the problem of model-based reasoning in ethics, especially pertaining to science and technology, and stress some aspects of model-based reasoning in technological innovation.

The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding 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 philosophy, 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. The term model' comprises both internal and external representations. The models are intended as interpretations 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. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.

The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology, and are logically and computationally oriented: the most recent results and achievements about the topics above are illustrated in detail in the papers.

This volume is based on the papers that were presented at the International Conference Model-Based Reasoning: Scientific Discovery, Technological Innovation, Values' (MBR'01), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in May 2001. The previous volume Model-Based Reasoning in Scientific Discovery, edited by L. Magnani, N.J. Nersessian, and P. Thagard (Kluwer Academic/Plenum Publishers, New York, 1999; Chinese edition, China Science and Technology Press, Beijing, 2000), was based on the papers presented at the first model-based reasoning' international conference, held at the same venue in December 1998.

The presentations given at the Conference explore how scientific thinking uses models and exploratory reasoning to produce creative changes in theories and concepts. Some address the problem of model-based reasoning in ethics, especially pertaining to science and technology, and stress some aspects of model-based reasoning in technological innovation.

The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding 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 philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitivescience.

There are several key ingredients common to the various forms of model-based reasoning. The term model' comprises both internal and external representations. The models are intended as interpretations 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. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.

The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology, and are logically and computationally oriented: the most recent results and achievements about the topics above are illustrated in detail in the papers.

There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term model comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book 's contributors are researchers active in the area of creative reasoning in science and technology.

The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of creative changes in science. The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning which cannot be described by classical logic alone. The study of these high-level methods of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic: at the heart of cognitive science. Model based reasoning promotes conceptual change because it is effective in abstracting, generating, and integrating constraints in ways that produce novel results. There are several key ingredients common to the various forms of model-based reasoning to be considered in this presentation. The models are intended as interpretations 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. In the modeling process, various forms of abstraction, such as limiting case, idealization, generalization, and generic modeling are utilized. Evaluation and adaptation take place in the light of structural of structural, causal, and/or functional constraint satisfaction and enhanced understanding of the target problem is obtained through the modeling process. Simulation can be used to produce new states and enable evaluation of behaviors, constraint satisfaction, and other factors. The book also addresses some of the main aspects of the concept of abduction, connecting it to the centralepistemological question of hypothesis withdrawal in science and model-based reasoning, where abductive interferences exhibit their most appealing cognitive virtues. The most recent results and achievements in the above areas are illustrated in detail by the various contributors to the work, who are among the most respected researchers in philosophy, artificial intelligence and cognitive science.

Einstein often expressed the sentiment that "the eternal mystery of the world is its comprehensibility," and that science is the means through which we comprehend it. However, nearly every one - including scientists - agrees that the concepts of modem physics are quite incomprehensible: They are both unintelligible to the educated lay-person and to the scientific community itself, where there is much dispute over the interpretation of even (and especially) the most basic concepts. There is, of course, almost universal agreement that modem science quite adequately accounts for and predicts events, i. e., that its calculations work better than those of classical physics; yet the concepts of science are supposed to be descriptive of 'the world' as well - they should enable us to comprehend it. So, it is asked, and needs tobe"asked: Has modem physics failed in an important respect? It failed with me as a physics student. I came to physics, as with most naIve students, out of a desire to know what the world is really like; in particular, to understand Einstein's conception of it. I thought I had grasped the concepts in classical mechanics, but with electrodynamics confusion set in and only increased with relativity and quantum mechanics. At that point I began even to doubt whether I had really understood the basic concepts of classical mechanics."

For some time now the philosophy of science has been undergoing a major transfor mation. It began when the 'received view' of scientific knowledge -that developed by logical positivists and their intellectual descendants - was challenged as bearing little resemblance to and having little relevance for the understanding of real science. Subsequently, an overwhelming amount of criticism has been added. One would be hard-pressed to find anyone who would support the 'received view' today. Yet, in the search for a new analysis of scientific knowledge, this view continues to exert influence over the tenor of much of present-day philosophy of science; in particular, over its problems and its methods of analysis. There has, however, emerged an area within the discipline - called by some the 'new philosophy of science' - that has been engaged in transforming the problems and methods of philosophy of science. While there is far from a consensus of beliefs in this area, most of the following contentions would be affirmed by those working in it: - that science is an open-ended, on-going activity, whose character has changed significantly during its history - that science is not a monolithic enterprise - that good science can lead to false theories - that science has its roots in everyday circumstances, needs, methods, concepts, etc."

Einstein often expressed the sentiment that "the eternal mystery of the world is its comprehensibility," and that science is the means through which we comprehend it. However, nearly every one - including scientists - agrees that the concepts of modem physics are quite incomprehensible: They are both unintelligible to the educated lay-person and to the scientific community itself, where there is much dispute over the interpretation of even (and especially) the most basic concepts. There is, of course, almost universal agreement that modem science quite adequately accounts for and predicts events, i. e., that its calculations work better than those of classical physics; yet the concepts of science are supposed to be descriptive of 'the world' as well - they should enable us to comprehend it. So, it is asked, and needs tobe"asked: Has modem physics failed in an important respect? It failed with me as a physics student. I came to physics, as with most naIve students, out of a desire to know what the world is really like; in particular, to understand Einstein's conception of it. I thought I had grasped the concepts in classical mechanics, but with electrodynamics confusion set in and only increased with relativity and quantum mechanics. At that point I began even to doubt whether I had really understood the basic concepts of classical mechanics."