• Publications
  • Influence
Model Growth
The algorithmic method of inductive inference that Ray Solomonoff proposes in (Solomonoff, 1964) is not interactive. Marcus Hutter defines how to add interactivity to the inductive method based onExpand
Universal Intelligence: A Definition of Machine Intelligence
A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantlyExpand
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffectiveExpand
Universal Artificial Intellegence - Sequential Decisions Based on Algorithmic Probability
  • Marcus Hutter
  • Computer Science
  • Texts in Theoretical Computer Science. An EATCS…
  • 2005
In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feelExpand
Universal Artificial Intellegence - Sequential Decisions Based on Algorithmic Probability
  • Marcus Hutter
  • Computer Science
  • Texts in Theoretical Computer Science. An EATCS…
  • 2004
universal artificial intelligence sequential decisions towards a universal theory of artificial intelligence universal artificial intelligence sequential decisions towards a universal theory ofExpand
A Collection of Definitions of Intelligence
This chapter is a survey of a large number of informal definitions of “intelligence” that the authors have collected over the years. Naturally, compiling a complete list would be impossible as manyExpand
PAC Bounds for Discounted MDPs
We study upper and lower bounds on the sample-complexity of learning near-optimal behaviour in finite-state discounted Markov Decision Processes (mdps). We prove a new bound for a modified version ofExpand
Algorithmic information theory
  • Marcus Hutter
  • Computer Science, Mathematics
  • Scholarpedia
  • 6 March 2007
This article is a brief guide to the field of algorithmic information theory (AIT), its underlying philosophy, and the most important concepts. AIT arises by mixing information theory and computationExpand
A Monte-Carlo AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notionExpand
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