Computational Philosophy of ScienceBy applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics as the nature of concepts, hypothesis formation, analogy, and theory justification. |
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Activating concept ACTIVE MESSAGES ACTIVE RULES algorithms analogy application argued argument artificial intelligence attached-concepts background theories best explanation biological causal chapter classes of facts cognitive psychology complex computational account computational philosophy conceptual combination concerning consilience criteria Darwin data structures deductive described descriptive discovery discussion empirical enumerative induction evidence evolutionary epistemology example experimental explananda explanatory firing rule goals historical philosophy hypotheses important induction inference inferential practice justification kind Kuhn laws logical positivists mechanisms methodological motion_back natural selection normative principles notion obj_a obj_b obj_c observations paradigm phenomena philosophy of science phlogiston theory PI's predicate calculus problem solving proj_true hypothesis0 propagate pseudoscience r_10_sound realism reflective equilibrium relevant resemblance thinking role schemas scientific knowledge scientific realism scientific theories scientists simplicity simulation slot solution spreading activation superordinate T₁ theory choice theory evaluation theory explains theory of sound tion triggered true truth variation wave theory