This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: online learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory,...

From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning covers several aspects of the "optimism in the face of uncertainty" principle for large scale optimization problems under finite numerical budget. The monograph's initial motivation came from the empirical success of the so-called "Monte-Carlo Tree Search" method popularized in Computer Go and further extended to many other games as well as optimization and planning problems. It lays out the theoretical foundations of the field by characterizing the complexity of the optimization problems...