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

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Full Articles. Table of Contents Project to Build Programs that Understand . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Eric Baum CHS-Soar: Introducing Constrained Heuristic Search to the Soar Cognitive Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Sean Bittle, Mark Fox In Search of Computational Correlates of Artificial Qualia. . . . . . . . . . . . . . 13 Antonio Chella, Salvatore Gaglio Combining Analytical and Evolutionary Inductive Programming . . . . . . . . 19 Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, Ute Schmid The China-Brain Project: Report on the First Six Months. . . . . . . . . . . . . . 25 Hugo de Garis AGI Preschool: A Framework for Evaluating Early-Stage Human-like AGIs 31 Ben Goertzel, Stephan Bugaj Pointer Semantics with Forward Propagation . . . . . . . . . . . . . . . . . . . . . . . . . 37 Sujata Ghosh, Benedikt Löwe, Sanchit Saraf The Role of Logic in AGI Systems: Towards a Lingua Franca for General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Helmar Gust, Ulf Krumnack, Angela Schwering, Kai-Uwe Kuehnberger The robotics path to AGI using Servo Stacks . . . . . . . . . . . . . . . . . . . . . . . . . 49 John Hall A Unifying Framework for Analysis and Evaluation of Inductive Programming Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Martin Hofmann, Emanuel Kitzelmann, Ute Schmid Feature Markov Decision Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Marcus Hutter Feature Dynamic Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Marcus Hutter Economic Attention Networks: Associative Memory and Resource Allocation for General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Matthew Ikle, Joel Pitt, Ben Goertzel, George Sellman
A formal framework for the symbol grounding problem . . . . . . . . . . . . . . . . 79 Benjamin Johnston, Mary-Anne Williams A Cognitive Map for an Artificial Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Unmesh Kurup, B. Chandrasekaran Claims and Challenges in Evaluating Human-Level Intelligent Systems . . . 91 John Laird, Robert Wray, Robert Marinier, Pat Langley Extending Cognitive Architectures with Mental Imagery . . . . . . . . . . . . . . . 97 Scott Lathrop, John Laird A Comparative Approach to Understanding General Intelligence: Predicting Cognitive Performance in an Open-ended Dynamic Task . . . . . 103 Christian Lebiere, Cleotilde Gonzalez, Walter Warwick Incorporating Planning and Reasoning into a Self-Motivated, Communicative Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Daphne Liu, Lenhart Schubert Program Representation for General Intelligence . . . . . . . . . . . . . . . . . . . . . . 114 Moshe Looks, Ben Goertzel Consciousness in Human and Machine: A Theory and Some Falsifiable Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Richard Loosemore Hebbian Constraint on the Resolution of the Homunculus Fallacy Leads to a Network that Searches for Hidden Cause-Effect Relationships . . . . . . . 126 Andras Lorincz Everyone’s a Critic: Memory Models and Uses for an Artificial Turing Judge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Joseph MacInnes, Blair Armstrong, Dwayne Pare, George Cree, Steve Joordens Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Matthew Miller, Peter Wong, Alexander Stoytchev Parsing PCFG within a General Probabilistic Inference Framework . . . . . . 144 Arthi Murugesan, Nicholas Cassimatis Self-Programming: Operationalizing Autonomy . . . . . . . . . . . . . . . . . . . . . . . 150 Eric Nivel, Kristinn R. Thorisson Bootstrap Dialog: A Conversational English Text Parsing and Generation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Stephen Reed vii
Page 2 and 3: Ben Goertzel, Pascal Hitzler, Marcu
Page 4 and 5: Artificial General Intelligence Vol
Page 6: Preface Artificial General Intellig
Page 9: Organizing Committee Tsvi Achler U.
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
Page 71 and 72:
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
Page 77 and 78:
is the best observation summary, wh
Page 79 and 80:
to a code for the rewards only, whi
Page 81 and 82:
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
Page 87 and 88:
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
Page 93 and 94:
Of course, Harnad’s argument is n
Page 95 and 96:
By exploring variations of Harnad
Page 97 and 98:
Discussion The representational sys
Page 99 and 100:
the cognitive map is less of a map
Page 101 and 102:
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
Page 119 and 120:
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
Page 139 and 140:
Hebbian Constraint on the Resolutio
Page 141 and 142:
The Case of Inputs that Change by T
Page 143 and 144:
This route is relevant if the noise
Page 145 and 146:
1. Oculus Info. Inc. Toronto, Ont.
Page 147 and 148:
Our implemented Turing judge used a
Page 149 and 150:
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
Page 179 and 180:
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
Page 191 and 192:
to experience” can be argued to b
Page 193 and 194:
Abstract Case-by-case Problem Solvi
Page 195 and 196:
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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