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

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Ben Goertzel, Pascal Hitzler, Marcu
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Artificial General Intelligence Vol
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Preface Artificial General Intellig
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Organizing Committee Tsvi Achler U.
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A formal framework for the symbol g
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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: ��
Page 65: � ��
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
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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
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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)
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UnsupervisedSegmentationofAudioSpee
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FinallyweusedthediscreteFourierTran
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Secondly,thereexistsmorethanonelogi
Page 157 and 158:
Parsing PCFG within a General Proba
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known in advance, although many imp
Page 161 and 162:
Figure 2: Shows the underlying weig
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Self-Programming: Operationalizing
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expressivity. More over, self-progr
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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
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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
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Human and Machine Understanding Of
Page 183 and 184:
case orderings t correspond to the
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in (1) received a different denotat
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Abstract This paper analyzes the di
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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
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Finally, the facts that both seem t
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differ. NARS uses Narsese, the fair
Page 205 and 206:
Integrating Action and Reasoning th
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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
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Distribution of Environments in For
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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
Page 229 and 230:
Importing Space-time Concepts Into
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HELEN: Using Brain Regions and Mech
Page 233 and 234:
Holistic Intelligence: Transversal
Page 235 and 236:
Achieving Artificial General Intell
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Achler, Tsvi . . . . . . . . . . .
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