Advances in Intelligent Systems Research - of Marcus Hutter

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In Memoriam Ray Solomonoff (1926-2009) The Great Ray Solomonoff, pioneer of Machine Learning, founder of Algorithmic Probability Theory, father of the Universal Probability Distribution, creator of the Universal Theory of Inductive Inference, passed away on Monday 30 November 2009, from complications in the wake of a broken aneurism in his head. He is survived by his loving wife, Grace. Ray Solomonoff was the first to describe the fundamental concept of Algorithmic Information or Kolmogorov Complexity, and the first to prove the celebrated Invariance Theorem. In the new millennium his work became the foundation of the first mathematical theory of Optimal Universal Artificial Intelligence. Ray intended to deliver an invited lecture at AGI 2010, the Conference on Artificial General Intelligence (March 5-8, 2010) in Lugano (where he already spent time in 2001 as a visiting professor at the Swiss AI Lab IDSIA). The AGI conference series would not even exist without his essential theoretical contributions. With great sadness AGI 2010 was held «In Memoriam Ray Solomonoff». Ray will live on in the many minds shaped by his revolutionary ideas. Eulogy by Jürgen Schmidhuber
This book is part of the series Advances in Intelligent Systems Research (ISSN: 1951-6851) published by Atlantis Press. Aims and scope of the series During the past decade computer science research in understanding and reproducing human intelligence has expanded from the more traditional approaches like psychology, logics and artificial intelligence into multiple other areas, including neuroscience research. Moreover, new results in biology, chemistry, (surface) physics and gene technology, but also in network technology are greatly affecting current research in computer science, including the development of intelligent systems. At the same time, computer science’s new results are increasingly being applied in these fields allowing for important cross-fertilisations. This series aims at publishing proceedings from all disciplines dealing with and affecting the issue of understanding and reproducing intelligence in artificial systems. Also, the series is open for publications concerning the application of intelligence in networked or any other environment and the extraction of meaningful data from large data sets. Research fields covered by the series include: * Fuzzy sets * Machine learning * Autonomous agents * Evolutionary systems * Robotics and autonomous systems * Semantic web, incl. web services, ontologies and grid computing * Biological systems * Artificial Intelligence, incl. knowledge representation, logics * Neural networks * Constraint satisfaction * Computational biology * Information sciences * Computer vision, pattern recognition * Computational neuroscience * Datamining, knowledge discovery and modelling for e.g. life sciences. © ATLANTIS PRESS, 2010 http://www.atlantis-press.com ISBN: 978-90-78677-36-9 This book is published by Atlantis Press, scientific publishing, Paris, France. All rights reserved for this book. No part of this book may be reproduced, translated, stored or transmitted in any form or by any means, including electronic, mechanical, photocopying, recording or otherwise, without prior permission from the publisher. Atlantis Press adheres to the creative commons policy, which means that authors retain the copyright of their article.
Page 2 and 3: Eric Baum, Marcus Hutter, Emanuel K
Page 6: Artificial General Intelligence Vol
Page 10 and 11: Conference Organization Chairs Marc
Page 12 and 13: Table of Contents Full Articles. Ef
Page 14: Uncertain Spatiotemporal Logic for
Page 17 and 18: inference. Constraint graphs compac
Page 19 and 20: s ← the rule system‟s opinion o
Page 21 and 22: Run Time (sec) Run Time (sec) probl
Page 23 and 24: ut also, more importantly, by the c
Page 25 and 26: pattern recognition only, while at
Page 27 and 28: Central would be a two-way interact
Page 29 and 30: set of the OpenCogPrime architectur
Page 31 and 32: mentioned elements to the real elem
Page 33 and 34: Suppose it has previously been show
Page 35 and 36: man reality; we have given a semi-f
Page 37 and 38: t∑ Vµ,g,T π ≡ E( r g (I g,s,i
Page 39 and 40: as we have formalized it here is sp
Page 41 and 42: s1 s2 s3 s4 s5 s6 s7 1 0 1 0 1 0 1
Page 43 and 44: valued for every τ and this value
Page 45 and 46: Environment Type General Bounded Ba
Page 47 and 48: The sliding window is passed over t
Page 49 and 50: data. However, TP alone performs ve
Page 51 and 52: Extension to Non-Symbolic Data Stri
Page 53 and 54: agent’s uncertain reasoning, than
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Theorem 2. Suppose that in addition
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List lnheritance $E $C Inheritance
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The Toy Box Problem As with existin
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the conceptual mismatch between the
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Initial 2D World State Impact in 2D
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R Rewriting Rule: a b a R b a b b
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ements connected by binary row and
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White uses E Black uses E Gomoku 78
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Approach We have used a NARMAX appr
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Range [cm] Range [cm] as well as th
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system with the computed rotational
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(a) (b) Figure 1: DCT network repre
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(a) Pole balancing (b) T-maze (c) B
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lated weights, i.e. requiring the f
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In this paper, we will discuss heur
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Consider again a substitution θ as
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(Ax S ) ∗∗ (Ax + j S S )∗∗
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size of the grid grows. Proposition
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Algorithm 2 Propagate Procedure Pro
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#relations MiniMaxSAT DPLL-S 5 0.9s
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is useful for designing the perform
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its knowledge is limited, and even
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RISC vs. CISC trade-offs in traditi
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Figure 1: Squares: algorithmic comp
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10 5 0 −5 −10 20 40 60 80 100 1
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References [Bas06] A. J. Bastian. L
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Our algorithm incorporates gradient
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is off-policy λ-return and ¯φ t
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we can substitute δ t e t , based
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LP1 Sensing a world state world_sta
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given observed face was considered
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analysis (verification) as has been
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Core Modules Five core regions in t
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agent. These modules receive instru
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The image processing done to extrac
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An agent-environment perception is
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for only one type of the sub-events
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where c ′ is the confidence of th
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sirability of events, i.e. such tha
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where r G , r P and r Q are the rew
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The rest of the argument parallels
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probability distribution Pr are onl
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Figure 1: (a-b) Two causal networks
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2.5 2.5 2 2 d(t) [bits] 1.5 1 d(t)
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eak this clique and then learning i
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The position of the image plane at
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The next experiments are performed
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was poorly aligned to human intelli
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claim that the goals of AGI are out
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feedback connections, pages 95-133.
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A non-universal variant (WS96) is r
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probability density 0.5 0.4 0.3 0.2
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due to the fact that the encoding l
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the “Four Big F’s”: Feeding,
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A runtime-dependent performance mea
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A. N. Kolmogorov. Three approaches
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describable regularity in a batch o
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start out with problems that are in
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This CJS estimate makes it easy to
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Frontier Search Sun Yi, Tobias Glas
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program i execution time τ steps i
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Example 12. Consider the criterion
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The Evaluation of AGI Systems Pei W
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telligence, the evaluation needs to
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Now we see that the empirical appro
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Designing a Safe Motivational Syste
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non-problematic result than explora
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architecture based upon Sloman’s
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Software Design of an AGI System Ba
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A Theoretical Framework to Formaliz
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Uncertain Spatiotemporal Logic for
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A (hopefully) Unbiased Universal En
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Neuroethological Approach to Unders
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Compression Progress, Pseudorandomn
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Relational Local Iterative Compress
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Stochastic Grammar Based Incrementa
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Compression-Driven Progress in Scie
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Concept Formation in the Ouroboros
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On Super-Turing Computing Power and
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A minimum relative entropy principl
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Author Index Araujo, Samir . . . .
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