Advances in Intelligent Systems Research - of Marcus Hutter

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Table of Contents Full Articles. Efficient Constraint-Satisfaction in Domains with Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Perrin Bignoli, Nicholas Cassimatis, Arthi Murugesan The CHREST Architecture of Cognition: The Role of Perception in General Intelligence . 7 Fernand Gobet, Peter Lane A General Intelligence Oriented Architecture for Embodied Natural Language Processing 13 Ben Goertzel, Cassio Pennachin, Samir Araujo, Ruiting Lian, Fabricio Silva, Murilo Queiroz, Welter Silva, Mike Ross, Linas Vepstas, Andre Senna Toward a Formal Characterization of Real-World General Intelligence . . . . . . . . . . . . . . . . . 19 Ben Goertzel On Evaluating Agent Performance in a Fixed Period of Time . . . . . . . . . . . . . . . . . . . . . . . . . 25 Jose Hernandez-Orallo Artificial General Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Daniel Hewlett, Paul Cohen Grounding Possible Worlds Semantics in Experiential Semantics . . . . . . . . . . . . . . . . . . . . . . 37 Matthew Ikle, Ben Goertzel The Toy Box Problem (and a Preliminary Solution) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Benjamin Johnston Playing General Structure Rewriting Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Lukasz Kaiser, Lukasz Stafiniak Towards Automated Code Generation for Autonomous Mobile Robots . . . . . . . . . . . . . . . . . 55 Dermot Kerr, Ulrich Nehmzow, Stephen A. Billings Searching for Minimal Neural Networks in Fourier Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Jan Koutnik, Faustino Gomez, Juergen Schmidhuber Remarks on the Meaning of Analogical Relations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Ulf Krumnack, Helmar Gust, Angela Schwering, Kai-Uwe Kuehnberger Quantitative Spatial Reasoning for General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Unmesh Kurup, Nicholas Cassimatis Cognitive Architecture Requirements for Achieving AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 John Laird, Robert Wray Sketch of an AGI Architecture with Illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Andras Lorincz, Zoltan Bardosi, Daniel Takacs
GQ(λ): A General Gradient Algorithm for Temporal-Difference Prediction Learning with Eligibility Traces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Hamid Maei, Richard Sutton A Generic Adaptive Agent Architecture Integrating Cognitive and Affective States and their Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Zulfiqar A. Memon, Jan Treur A Cognitive Architecture for Knowledge Exploitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Gee Wah Ng, Yuan Sin Tan, Loo Nin Teow, Khin Hua Ng, Kheng Hwee Tan, Rui Zhong Chan An Artificial Intelligence Model that Combines Spatial and Temporal Perception . . . . . . . . 109 Jianglong Nan, Fintan Costello A Conversion Between Utility and Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Pedro Ortega, Daniel Braun A Bayesian Rule for Adaptive Control based on Causal Interventions . . . . . . . . . . . . . . . . . . 121 Pedro Ortega, Daniel Braun Discovering and Characterizing Hidden Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Soumi Ray, Tim Oates What we Might Look for in an AGI Benchmark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Brandon Rohrer Towards Practical Universal Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Tom Schaul, Juergen Schmidhuber Artificial Scientists & Artists Based on the Formal Theory of Creativity . . . . . . . . . . . . . . . 145 Juergen Schmidhuber Algorithmic Probability, Heuristic Programming and AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Ray Solomonoff Frontier Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Yi Sun, Tobias Glasmachers, Tom Schaul, Juergen Schmidhuber The Evaluation of AGI Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Pei Wang Designing a Safe Motivational System for Intelligent Machines . . . . . . . . . . . . . . . . . . . . . . . . 170 Mark Waser Position Statements. Software Design of an AGI System Based on Perception Loop . . . . . . . . . . . . . . . . . . . . . . . . 176 Antonio Chella, Massimo Cossentino, Valeria Seidita A Theoretical Framework to Formalize AGI-Hard Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Pedro Demasi, Jayme Szwarcfiter, Adriano Cruz
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 14: Uncertain Spatiotemporal Logic for
Page 17 and 18: inference. Constraint graphs compac
Page 19 and 20: s ← the rule system‟s opinion o
Page 21 and 22: Run Time (sec) Run Time (sec) probl
Page 23 and 24: ut also, more importantly, by the c
Page 25 and 26: pattern recognition only, while at
Page 27 and 28: Central would be a two-way interact
Page 29 and 30: set of the OpenCogPrime architectur
Page 31 and 32: mentioned elements to the real elem
Page 33 and 34: Suppose it has previously been show
Page 35 and 36: man reality; we have given a semi-f
Page 37 and 38: t∑ Vµ,g,T π ≡ E( r g (I g,s,i
Page 39 and 40: as we have formalized it here is sp
Page 41 and 42: s1 s2 s3 s4 s5 s6 s7 1 0 1 0 1 0 1
Page 43 and 44: valued for every τ and this value
Page 45 and 46: Environment Type General Bounded Ba
Page 47 and 48: The sliding window is passed over t
Page 49 and 50: data. However, TP alone performs ve
Page 51 and 52: Extension to Non-Symbolic Data Stri
Page 53 and 54: agent’s uncertain reasoning, than
Page 55 and 56: Theorem 2. Suppose that in addition
Page 57 and 58: List lnheritance $E $C Inheritance
Page 59 and 60: The Toy Box Problem As with existin
Page 61 and 62: the conceptual mismatch between the
Page 63 and 64:
Initial 2D World State Impact in 2D
Page 65 and 66:
R Rewriting Rule: a b a R b a b b
Page 67 and 68:
ements connected by binary row and
Page 69 and 70:
White uses E Black uses E Gomoku 78
Page 71 and 72:
Approach We have used a NARMAX appr
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Range [cm] Range [cm] as well as th
Page 75 and 76:
system with the computed rotational
Page 77 and 78:
(a) (b) Figure 1: DCT network repre
Page 79 and 80:
(a) Pole balancing (b) T-maze (c) B
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lated weights, i.e. requiring the f
Page 83 and 84:
In this paper, we will discuss heur
Page 85 and 86:
Consider again a substitution θ as
Page 87 and 88:
(Ax S ) ∗∗ (Ax + j S S )∗∗
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size of the grid grows. Proposition
Page 91 and 92:
Algorithm 2 Propagate Procedure Pro
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#relations MiniMaxSAT DPLL-S 5 0.9s
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is useful for designing the perform
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its knowledge is limited, and even
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RISC vs. CISC trade-offs in traditi
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Figure 1: Squares: algorithmic comp
Page 103 and 104:
10 5 0 −5 −10 20 40 60 80 100 1
Page 105 and 106:
References [Bas06] A. J. Bastian. L
Page 107 and 108:
Our algorithm incorporates gradient
Page 109 and 110:
is off-policy λ-return and ¯φ t
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we can substitute δ t e t , based
Page 113 and 114:
LP1 Sensing a world state world_sta
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given observed face was considered
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analysis (verification) as has been
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Core Modules Five core regions in t
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agent. These modules receive instru
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The image processing done to extrac
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An agent-environment perception is
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for only one type of the sub-events
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where c ′ is the confidence of th
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sirability of events, i.e. such tha
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where r G , r P and r Q are the rew
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The rest of the argument parallels
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probability distribution Pr are onl
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Figure 1: (a-b) Two causal networks
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2.5 2.5 2 2 d(t) [bits] 1.5 1 d(t)
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eak this clique and then learning i
Page 145 and 146:
The position of the image plane at
Page 147 and 148:
The next experiments are performed
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was poorly aligned to human intelli
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claim that the goals of AGI are out
Page 153 and 154:
feedback connections, pages 95-133.
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A non-universal variant (WS96) is r
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probability density 0.5 0.4 0.3 0.2
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due to the fact that the encoding l
Page 161 and 162:
the “Four Big F’s”: Feeding,
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A runtime-dependent performance mea
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A. N. Kolmogorov. Three approaches
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describable regularity in a batch o
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start out with problems that are in
Page 171 and 172:
This CJS estimate makes it easy to
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Frontier Search Sun Yi, Tobias Glas
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program i execution time τ steps i
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Example 12. Consider the criterion
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The Evaluation of AGI Systems Pei W
Page 181 and 182:
telligence, the evaluation needs to
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Now we see that the empirical appro
Page 185 and 186:
Designing a Safe Motivational Syste
Page 187 and 188:
non-problematic result than explora
Page 189 and 190:
architecture based upon Sloman’s
Page 191 and 192:
Software Design of an AGI System Ba
Page 193 and 194:
A Theoretical Framework to Formaliz
Page 195 and 196:
Uncertain Spatiotemporal Logic for
Page 197 and 198:
A (hopefully) Unbiased Universal En
Page 199 and 200:
Neuroethological Approach to Unders
Page 201 and 202:
Compression Progress, Pseudorandomn
Page 203 and 204:
Relational Local Iterative Compress
Page 205 and 206:
Stochastic Grammar Based Incrementa
Page 207 and 208:
Compression-Driven Progress in Scie
Page 209 and 210:
Concept Formation in the Ouroboros
Page 211 and 212:
On Super-Turing Computing Power and
Page 213 and 214:
A minimum relative entropy principl
Page 215:
Author Index Araujo, Samir . . . .
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Advances in Intelligent Systems Research - of Marcus Hutter

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