A Framework for Evaluating Early-Stage Human - of Marcus Hutter

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Artificial General Intelligence Volume Editors Ben Goertzel Novamente & Biomind LLC 1405 Bernerd Place Rockville MD 20851, USA Email: ben@goertzel.org www: http://www.goertzel.org Pascal Hitzler AIFB Universität Karlsruhe (TH) Kollegiengebäude am Ehrenhof 11.40, Room 224 Englerstraße 11 76131 Karlsruhe, Germany Email: pascal@pascal-hitzler.de www: http://www.pascal-hitzler.de Marcus Hutter CSL@RSISE and SML@NICTA Australian National University Room B259, Building 115 Corner of North and Daley Road Canberra ACT 0200, Australia Email: marcus.hutter@gmx.net www: http://www.hutter1.net
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 crossfertilisations. 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, 2009 http://www.atlantis-press.com ISBN: 978-90-78677-24-6 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: Ben Goertzel, Pascal Hitzler, Marcu
Page 6: Preface Artificial General Intellig
Page 9 and 10: Organizing Committee Tsvi Achler U.
Page 11 and 12: A formal framework for the symbol g
Page 13 and 14: Importing Space-time Concepts Into
Page 15 and 16: one structure, everything else adap
Page 17 and 18: generalize is essential to understa
Page 19 and 20: First Application The above framewo
Page 21 and 22: problem solving, use of knowledge,
Page 23 and 24: Of particular note is the separatio
Page 25 and 26: Conclusions and Future Work While p
Page 27 and 28: Conceptual Spaces The conceptual ar
Page 29 and 30: perceptions and actions. The lingui
Page 31 and 32: means of the knowledge stored in th
Page 33 and 34: grams given some (syntactically res
Page 35 and 36: For example, let reverse([]) = [] r
Page 37 and 38: Igor2 produced solutions with auxil
Page 39 and 40: evolve neural net modules as quickl
Page 41 and 42: ain”, consisting of some dozen or
Page 43 and 44: Population Chr NListPtr m Chrm is a
Page 45 and 46: and shaping, we feel that exploring
Page 47 and 48: Table 2 reviews the key capabilitie
Page 49 and 50: One way to achieve this goal would
Page 51 and 52: question. Our approach of staying a
Page 53 and 54: H0 δB(H0) δF(H0) H1 δB(H1) δF(H
Page 55 and 56:
Discussion and future work Testing
Page 57 and 58:
Types of Reasoning Corresponding Fo
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Integrating Different Types of Reas
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good overview of analogy models can
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Page 67 and 68:
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Page 69 and 70:
A Unified Framework for IP Conditio
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negative evidence when added to a r
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ing strategy. Due to GOLEM’s rand
Page 75 and 76:
any state index, n∈IN the current
Page 77 and 78:
is the best observation summary, wh
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to a code for the rewards only, whi
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have exponentially many states (2O(
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where ˆσ 2 =Loss( ˆw)/n. Given
Page 85 and 86:
• The cost function can be improv
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decides to introduce inflation or d
Page 89 and 90:
€ € The diffusion matrix is the
Page 91 and 92:
tuning may be done adaptively by te
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Of course, Harnad’s argument is n
Page 95 and 96:
By exploring variations of Harnad
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Discussion The representational sys
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the cognitive map is less of a map
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models of cognition except in cases
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7(b), we can see that the straight
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eaction times, error rates, or exac
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comparing behavior with and without
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Doorenbos, R. B. 1994. Combining le
Page 111 and 112:
egion, we leave the type of object
Page 113 and 114:
extract features in support of reco
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with a minimum score of -1.0. The
Page 117 and 118:
the NASA Human Error Modeling compa
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whether their models are capable of
Page 121 and 122:
Incorporating Planning and Reasonin
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see an unclaimed ten-dollar bill al
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swering user queries) and the state
Page 127 and 128:
Program Representation for General
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distribution P corresponding to the
Page 131 and 132:
with all Ei replaced by the unbound
Page 133 and 134:
Consciousness in Human and Machine:
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at the sensory receptors, is pre-pr
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The second choice is to take a deta
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Hebbian Constraint on the Resolutio
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The Case of Inputs that Change by T
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This route is relevant if the noise
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1. Oculus Info. Inc. Toronto, Ont.
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Our implemented Turing judge used a
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2.7; Human vs. JabberWacky t(157.6)
Page 151 and 152:
UnsupervisedSegmentationofAudioSpee
Page 153 and 154:
FinallyweusedthediscreteFourierTran
Page 155 and 156:
Secondly,thereexistsmorethanonelogi
Page 157 and 158:
Parsing PCFG within a General Proba
Page 159 and 160:
known in advance, although many imp
Page 161 and 162:
Figure 2: Shows the underlying weig
Page 163 and 164:
Self-Programming: Operationalizing
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expressivity. More over, self-progr
Page 167 and 168:
existence of a distance function ov
Page 169 and 170:
Bootstrap Dialog: A Conversational
Page 171 and 172:
elation is typically a verb. In the
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node RDF proposition N1 N2 N3 N4 N5
Page 175 and 176:
Analytical Inductive Programming as
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Problem domain: puttable(x) PRE: cl
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eq Hanoi(0, Src, Aux, Dst, S) = mov
Page 181 and 182:
Human and Machine Understanding Of
Page 183 and 184:
case orderings t correspond to the
Page 185 and 186:
in (1) received a different denotat
Page 187 and 188:
Abstract This paper analyzes the di
Page 189 and 190:
Having grounded meaning: In an inte
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to experience” can be argued to b
Page 193 and 194:
Abstract Case-by-case Problem Solvi
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First, since NARS is designed in th
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typical response is to find such an
Page 199 and 200:
What Is Artificial General Intellig
Page 201 and 202:
Finally, the facts that both seem t
Page 203 and 204:
differ. NARS uses Narsese, the fair
Page 205 and 206:
Integrating Action and Reasoning th
Page 207 and 208:
states, with the condition that the
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action model. The system is able to
Page 211 and 212:
Neuroscience and AI Share the Same
Page 213 and 214:
Relevance Based Planning: Why Its a
Page 215 and 216:
General Intelligence and Hypercompu
Page 217 and 218:
To appear, AGI-09 1 Stimulus proces
Page 219 and 220:
Distribution of Environments in For
Page 221 and 222:
The Importance of Being Neural-Symb
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Improving the Believability of Non-
Page 225 and 226:
Understanding the Brain’s Emergen
Page 227 and 228:
Abstract The challenge of creating
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Importing Space-time Concepts Into
Page 231 and 232:
HELEN: Using Brain Regions and Mech
Page 233 and 234:
Holistic Intelligence: Transversal
Page 235 and 236:
Achieving Artificial General Intell
Page 237:
Achler, Tsvi . . . . . . . . . . .
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