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Induction. Processes of Inference, Learning, and Discovery

Résumé :


Two psychologists, a computer scientist, and a philosopher have collaborated to present a framework for understanding processes of inductive reasoning and learning in organisms and machines, ranging in complexity from conditioning in rats to scientific discovery. Theirs is the first major effort to bring the ideas of several disciplines to bear on a subject that has been an active topic of investigation since the time of Socrates. The result is an integrated account that treats problem solving and induction in terms of rule­based mental models. This appoach is used to illuminate a wide range of topics that have previously been dealt with only by disparate and relatively narrow theories. – The twelve chapters cover topics such as generalization from instances, category induction, the reduction of uncertaincy, covariation detection in humans and animals, analogy, the discovery of scientific theories, and the construction of flexible computational systems. The authors’ approach is a pragmatic one that transcends the limitations of standard syntactic approaches by taking into account goals and problem-solving contexts. – Table of Contents : – 1. A Framework for Induction; – 2. Rule-Base Mental Models; – 3. The Modification of Rules; – 4. Computational Implementation of Inductive Systems; – 5. Conditioning and Covariation Detection; – 6. Category Formation; – 7. Modeling the Physical and Social Worlds; – 8. Generalization and Knowledge of Variability; – 9. Learning Inferential Rules; – 10. Analogy; – 11. Scientific Discovery; – 12. Epilogue: Toward a Theory of Induction. M.-M. V.


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Dernière mise à jour : Samedi 27 novembre 2021