Advances in Intelligent Systems Research - of Marcus Hutter

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Artificial General Intelligence Volume Editors 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 Eric B. Baum Azure Sky Research Inc. 386 Riverside Drive Princeton NJ 08540 USA Email: ebaum@fastmail.fm WWW: http://whatisthought.com/ Emanuel Kitzelmann Cognitive Systems Group, WIAI University of Bamberg Feldkirchenstrasse 21 D-96052 Bamberg, Germany Email: emanuel.kitzelmann@uni-bamberg.de WWW: http://www.uni-bamberg.de/kogsys/members/kitzelmann/
Page 2 and 3: Eric Baum, Marcus Hutter, Emanuel K
Page 4 and 5: In Memoriam Ray Solomonoff (1926-20
Page 8: Preface Artificial General Intellig
Page 11 and 12: Lyle Ungar Les Valiant Marco Wierin
Page 13 and 14: GQ(λ): A General Gradient Algorith
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:
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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
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distance m 12 10 8 6 4 2 0 backwar
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Remarks on the Meaning of Analogica
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unclear. If these generalized symbo
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. . ∧ height(water in vial, t 2 )
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Quantitative Spatial Reasoning for
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allows for the representation of sp
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SAT SAT-S #Near=1 #Near=2 #Near=3 #
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Cognitive Architecture Requirements
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so that the dynamics of the environ
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of task-specific knowledge it repre
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Sketch of an AGI architecture with
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50 100 150 200 50 100 150 200 50 10
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2 4 6 2 4 6 2 4 6 2 4 6 2 4 6 2 4 6
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GQ(λ): A general gradient algorith
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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
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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
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An Artificial Intelligence Model th
Page 126 and 127:
may refer to and thus be categorise
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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
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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
Page 162 and 163:
where the reward r(t) is a special
Page 164 and 165:
the first derivative of subjective
Page 166 and 167:
Algorithmic Probability, Heuristic
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Subjectivity Occasionally in making
Page 170 and 171:
Merging of this sort can be used on
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a larger project. Phase 2 (Sol03)
Page 174 and 175:
of the enumeration p : N → P. The
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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
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Wang, editors, Advance of Artificia
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since it can easily be subverted by
Page 188 and 189:
always logical, and that their cons
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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
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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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