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

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Chairs Conference Organization Ben Goertzel (Conference Chair) Novamente LLC, USA Stan Franklin (Honorary Chair) University of Memphis, USA Pascal Hitzler (Programme Chair) University of Karlsruhe, Germany Marcus Hutter (Programme Chair) Australian National University, Australia Programme Committee Itamar Arel University of Tennessee, Knoxville, USA Sebastian Bader Rostock University, Germany Eric Baum Baum Research Enterprises, USA Mark H. Bickhard Lehigh University, USA David Dowe Monash University, Australia Hugo de Garis Xiamen University, China Wlodek Duch Nicolaus Copernicus University, Poland Phil Goetz J. Craig Venter Institute, USA Artur Garcez City University London, UK Ben Goertzel Novamente LLC, USA Marco Gori University of Siena, Italy J. Storrs Hall Institute for Molecular Manufacturing, USA Hassan Mahmud Australian National University, Australia Cliff Joslyn Pacific Northwest National Laboratory, USA Kai-Uwe Kühnberger University of Osnabrck, Germany Christian Lebiere Carnegie Mellon University, USA Shane Legg University of Lugano, Switzerland Moshe Looks Google Research, USA Jose Hernandez Orallo Politechnical University of Valencia, Spain Jan Poland ABB Research, Zurich, Switzerland Sebastian Rudolph University of Karlsruhe, Germany Scott Sanner NICTA. Australia Ute Schmid Universiy of Bamberg, Germany Jürgen Schmidhuber IDSIA, Switzerland Angela Schwering University of Osnabrck, Germany Ray Solomonoff Oxbridge Research, Cambridge, USA Frank van der Velde Leiden University, The Netherlands Karin Verspoor University of Colorado, Denver, USA Marco Wiering University of Utrecht, The Netherlands Mary-Anne Williams University of Technology, Sydney, Australia
Organizing Committee Tsvi Achler U. of Illinois Urbana-Champaign, USA Itamar Arel University of Tennessee Knoxville, USA Sam S. Adams IBM Research, USA Wlodek Duch Nicolaus Copernicus University, Poland Sandra S. Hall AT&T Research, USA Pascal Hitzler University of Karlsruhe, Germany Marcus Hutter Australian National University, Australia Bruce Klein Novamente LLC, USA Stephen Reed Texai.org, USA External Reviewers Bobby Coop Benjamin Johnston Scott Livingston v
Page 2 and 3: Ben Goertzel, Pascal Hitzler, Marcu
Page 4 and 5: Artificial General Intelligence Vol
Page 6: Preface Artificial General Intellig
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
Page 59 and 60:
Integrating Different Types of Reas
Page 61 and 62:
good overview of analogy models can
Page 63 and 64:
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Page 65 and 66:
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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
Page 73 and 74:
ing strategy. Due to GOLEM’s rand
Page 75 and 76:
any state index, n∈IN the current
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is the best observation summary, wh
Page 79 and 80:
to a code for the rewards only, whi
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have exponentially many states (2O(
Page 83 and 84:
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
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By exploring variations of Harnad
Page 97 and 98:
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
Page 103 and 104:
7(b), we can see that the straight
Page 105 and 106:
eaction times, error rates, or exac
Page 107 and 108:
comparing behavior with and without
Page 109 and 110:
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
Page 115 and 116:
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
Page 123 and 124:
see an unclaimed ten-dollar bill al
Page 125 and 126:
swering user queries) and the state
Page 127 and 128:
Program Representation for General
Page 129 and 130:
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:
Page 135 and 136:
at the sensory receptors, is pre-pr
Page 137 and 138:
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
Page 143 and 144:
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
Page 165 and 166:
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
Page 173 and 174:
node RDF proposition N1 N2 N3 N4 N5
Page 175 and 176:
Analytical Inductive Programming as
Page 177 and 178:
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
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Abstract This paper analyzes the di
Page 189 and 190:
Having grounded meaning: In an inte
Page 191 and 192:
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
Page 197 and 198:
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
Page 209 and 210:
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
Page 223 and 224:
Improving the Believability of Non-
Page 225 and 226:
Understanding the Brain’s Emergen
Page 227 and 228:
Abstract The challenge of creating
Page 229 and 230:
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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A Framework for Evaluating Early-Stage Human - of Marcus Hutter

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