This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays provide engaging surveys of different, complementary, and mutually inspiring approaches to the topic, both from a philosophical and a mathematical viewpoint.

Building upon this base, subsequent papers present novel extensions of algorithmic learning theory as well as bold, new applications to traditional issues in epistemology and the philosophy of science. The volume is vital reading for students and researchers seeking a fresh, truth-directed approach to the philosophy of science and induction, epistemology, logic, and statistics.

to the Philosophy and Mathematics of Algorithmic Learning Theory.- to the Philosophy and Mathematics of Algorithmic Learning Theory.- Technical Papers.- Inductive Inference Systems for Learning Classes of Algorithmically Generated Sets and Structures.- Deduction, Induction, and beyond in Parametric Logic.- How Simplicity Helps You Find the Truth without Pointing at it.- Induction over the Continuum.- Philosophy Papers.- Logically Reliable Inductive Inference.- Some Philosophical Concerns about the Confidence in 'Confident Learning'.- How to Do Things with an Infinite Regress.- Trade-Offs.- Two Ways of Thinking about Induction.- Between History and Logic.