Bücher von Marcus Hutter

An Introduction to Universal Artificial Intelligence provides a gentle introduction to Universal Artificial Intelligence (UAI), a theory that provides a formal underpinning of what it means for an agent to act intelligently in a general class of environments. First presented in Universal Artificial Intelligence (Hutter, 2004), UAI presents a model in which most other problems in AI can be presented, and unifies ideas from sequential decision theory, Bayesian inference and information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI represents a theoretical bound on intelligent behaviour, and so we also discuss tractable approximations of this optimal agent.The book covers important practical approaches including efficient Bayesian updating with context tree weighting, and stochastic planning, approximated by sampling with Monte Carlo tree search. Algorithms are also included for the reader to implement, along with experimental results to compare against. This serves to approximate AIXI, as well as being used in state-of-the-art approaches in AI today. The book ends with a philosophical discussion of AGI covering the following key questions: Should intelligent agents be constructed at all, is it inevitable that they will be constructed, and is it dangerous to do so?This text is suitable for late undergraduates and includes an extensive background chapter to fill in the assumed mathematical background.

This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory (ALT 2007), which was held in Sendai (Japan) during October 1-4, 2007. The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference. The conference was co-located with the Tenth International Conference on Discovery Science (DS 2007). This volume includes 25 technical contributions that were selected from 50 submissions by the ProgramCommittee. It also contains descriptions of the ?ve invited talks of ALT and DS; longer versions of the DS papers are available in the proceedings of DS 2007. These invited talks were presented to the audience of both conferences in joint sessions.