Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence

Introduction to Ray Solomonoff 85th Memorial Conference.- Ray Solomonoff and the New Probability.- Universal Heuristics: How Do Humans Solve "Unsolvable" Problems?.- Partial Match Distance.- Falsification and Future Performance.- The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"?.- Inductive Inference and Partition Exchangeability in Classification.- Learning in the Limit: A Mutational and Adaptive Approach.- Algorithmic Simplicity and Relevance.- Categorisation as Topographic Mapping between Uncorrelated Spaces.- Algorithmic Information Theory and Computational Complexity.- A Critical Survey of Some Competing Accounts of Concrete Digital Computation.- Further Reflections on the Timescale of AI.- Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL.- Complexity Measures for Meta-learning and Their Optimality.- Design of a Conscious Machine.- No Free Lunch versus Occam's Razor in Supervised Learning.- An Approximation of the Universal Intelligence Measure.- Minimum Message Length Analysis of the Behrens-Fisher Problem.- MMLD Inference of Multilayer Perceptrons.- An Optimal Superfarthingale and Its Convergence over a Computable Topological Space.- Diverse Consequences of Algorithmic Probability.- An Adaptive Compression Algorithm in a Deterministic World.- Toward an Algorithmic Metaphysics.- Limiting Context by Using the Web to Minimize Conceptual Jump Size.- Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models.- Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction.- Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation.- On the Application of Algorithmic Probability to Autoregressive Models.- Principles of Solomonoff Induction and AIXI.- MDL/Bayesian Criteria Based on Universal Coding/Measure.- Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers.- (Non-)Equivalence ofUniversal Priors.- A Syntactic Approach to Prediction.- Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory.