Advances in Intelligent Systems Research - of Marcus Hutter

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Uncertain Spatiotemporal Logic for General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Nil Geisweiller, Ben Goertzel A (hopefully) Unbiased Universal Environment Class for Measuring Intelligence of Biological and Artificial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 Jose Hernandez-Orallo Neuroethological Approach to Understanding Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 DaeEun Kim Compression Progress, Pseudorandomness, and Hyperbolic Discounting . . . . . . . . . . . . . . . . 186 Moshe Looks Relational Local Iterative Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Laurent Orseau Stochastic Grammar Based Incremental Machine Learning Using Scheme . . . . . . . . . . . . . . 190 Eray Ozkural, Cevdet Aykanat Compression-driven Progress in Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Leo Pape Concept Formation in the Ouroboros Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 Knud Thomsen On Super-Turing Computing Power and Hierarchies of Artificial General Intelligence Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 Jiri Wiedermann A Minimum Relative Entropy Principle for AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Antoine van de Ven, Ben Schouten Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
Page 2 and 3: Eric Baum, Marcus Hutter, Emanuel K
Page 4 and 5: In Memoriam Ray Solomonoff (1926-20
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 16 and 17: Efficient Constraint-Satisfaction i
Page 18 and 19: three termination conditions is met
Page 20 and 21: any color. However, in many scenari
Page 22 and 23: The CHREST Architecture of Cognitio
Page 24 and 25: of look-ahead search as a function
Page 26 and 27: eing the maternal input used for tr
Page 28 and 29: A General Intelligence Oriented Arc
Page 30 and 31: own characteristics. For example, w
Page 32 and 33: Questioning and it contains the ele
Page 34 and 35: Toward a Formal Characterization of
Page 36 and 37: and the proving of interesting theo
Page 38 and 39: We suggest to view the definitions
Page 40 and 41: On Evaluating Agent Performance in
Page 42 and 43: Both the agent and the environment
Page 44 and 45: A first (and naïve) idea to avoid
Page 46 and 47: Artificial General Segmentation Dan
Page 48 and 49: Language VE and its variants have b
Page 50 and 51: TRAINING TEST Size P R F P R F 1.00
Page 52 and 53: Grounding Possible Worlds Semantics
Page 54 and 55: Grounding Possible Worlds Semantics
Page 56 and 57: To execute inferences using indefin
Page 58 and 59: The Toy Box Problem (and a Prelimin
Page 60 and 61: Hull Corner Mass Spring Figure 1: P
Page 62 and 63: Step 1 Step 2 Step 3 Step 4 Step 5
Page 64 and 65:
Playing General Structure Rewriting
Page 66 and 67:
choice of such logic is composition
Page 68 and 69:
Evaluation Game: (0, 0) (0, 1) 0.2
Page 70 and 71:
Towards Automated Code Generation f
Page 72 and 73:
ment. 2. Obtain environment models:
Page 74 and 75:
velocities of the human along the t
Page 76 and 77:
Searching for Minimal Neural Networ
Page 78 and 79:
Figure 2: Network representation an
Page 80 and 81:
distance m 12 10 8 6 4 2 0 backwar
Page 82 and 83:
Remarks on the Meaning of Analogica
Page 84 and 85:
unclear. If these generalized symbo
Page 86 and 87:
. . ∧ height(water in vial, t 2 )
Page 88 and 89:
Quantitative Spatial Reasoning for
Page 90 and 91:
allows for the representation of sp
Page 92 and 93:
SAT SAT-S #Near=1 #Near=2 #Near=3 #
Page 94 and 95:
Cognitive Architecture Requirements
Page 96 and 97:
so that the dynamics of the environ
Page 98 and 99:
of task-specific knowledge it repre
Page 100 and 101:
Sketch of an AGI architecture with
Page 102 and 103:
50 100 150 200 50 100 150 200 50 10
Page 104 and 105:
2 4 6 2 4 6 2 4 6 2 4 6 2 4 6 2 4 6
Page 106 and 107:
GQ(λ): A general gradient algorith
Page 108 and 109:
and where φ t is an alternate nota
Page 110 and 111:
We now turn to converting these for
Page 112 and 113:
A Generic Adaptive Agent Architectu
Page 114 and 115:
world sensor state w state for w th
Page 116 and 117:
• Adaptivity The issue of adaptiv
Page 118 and 119:
A Cognitive Architecture for Knowle
Page 120 and 121:
Figure 2: Three levels of Hierarchy
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The arrows in Figure 3 show how the
Page 124 and 125:
An Artificial Intelligence Model th
Page 126 and 127:
may refer to and thus be categorise
Page 128 and 129:
The sole purpose of this knowledge
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A conversion between utility and in
Page 132 and 133:
Utilities in Stochastic Processes I
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A Cross Negentropy Act. Negentropy
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A Bayesian Rule for Adaptive Contro
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elements in M given the past intera
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The inner sum has the form − ∑
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Discovering and characterizing Hidd
Page 144 and 145:
Figure 1: Our Network Architecture.
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plots the number of iterations and
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What we might look for in an AGI be
Page 150 and 151:
cheating for this to be done throug
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lytical and mathematical problems r
Page 154 and 155:
Towards Practical Universal Search
Page 156 and 157:
Furthermore, it is highly tolerant
Page 158 and 159:
T ′ has density function ∞∑
Page 160 and 161:
Artificial Scientists & Artists Bas
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where the reward r(t) is a special
Page 164 and 165:
the first derivative of subjective
Page 166 and 167:
Algorithmic Probability, Heuristic
Page 168 and 169:
Subjectivity Occasionally in making
Page 170 and 171:
Merging of this sort can be used on
Page 172 and 173:
a larger project. Phase 2 (Sol03)
Page 174 and 175:
of the enumeration p : N → P. The
Page 176 and 177:
Corollary 8. Assume there exists p
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Algorithm 2: Approximate Frontier S
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like human” and “think/act rati
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ligence to be fully self-consistent
Page 184 and 185:
Wang, editors, Advance of Artificia
Page 186 and 187:
since it can easily be subverted by
Page 188 and 189:
always logical, and that their cons
Page 190 and 191:
arguably, these machines are, accor
Page 192 and 193:
the results. The new PASSI allows u
Page 194 and 195:
Definition 11. The union of two ora
Page 196 and 197:
∫ 1 X Z (a) β Y X β Z (b) Figur
Page 198 and 199:
straints, if we decide to generate
Page 200 and 201:
systems and their sensorimotor coor
Page 202 and 203:
its are sufficient to encode the se
Page 204 and 205:
Lossless Compression Sketch Proof S
Page 206 and 207:
Update algorithms We have designed
Page 208 and 209:
however, allows for the implementat
Page 210 and 211:
is established well enough for imme
Page 212 and 213:
determines the switching times when
Page 214 and 215:
make the calculations easier. In fa
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Advances in Intelligent Systems Research - of Marcus Hutter

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