Advances in Intelligent Systems Research - of Marcus Hutter

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Conference Organization Chairs Marcus Hutter (Conference Chair) Jürgen Schmidhuber (Local Chair) Eric Baum (Program Chair) Emanuel Kitzelmann (Program Chair) Australian National University, Australia IDSIA, Switzerland Azure Sky Research Inc., USA University of Bamberg, Germany Program Committee Igor Aleksander Imperial College London, UK Lee Altenberg University of Hawaii at Manoa, USA Itamar Arel University of Tennessee, Knoxville, USA Sebastian Bader Rostock University, Germany Eric Baum Azure Sky Research Inc., USA Anselm Blumer Tufts University, USA Hugo de Garis Xiamen University, China Wlodek Duch Nicolaus Copernicus University, Poland Artur Garcez City University London, UK J. Storrs Hall Institute for Molecular Manufacturing, USA Benjamin Johnston University of Technology, Sydney, Australia Bert Kappen Radboud University, The Netherlands Emanuel Kitzelmann University of Bamberg, Germany Kai-Uwe Kühnberger University of Osnabrück, Germany Christian Lebiere Carnegie Mellon University, USA Shane Legg University College London, UK Moshe Looks Google Research, USA András Lörincz Eötvös Loránd University, Hungary Hassan Mahmud Australian National University, Australia Eric Nivel Reykjavik University, Iceland Jan Poland ABB Research, Zurich, Switzerland Brandon Rohrer Sandia National Laboratory, USA Sebastian Rudolph University of Karlsruhe, Germany Robert Schapire Princeton University, USA Lokendra Shastri Infosys Technologies Ltd, India Ray Solomono Oxbridge Research, Cambridge, USA Rich Sutton University of Alberta, Canada Kristinn Thorisson Reykjavik University, Iceland
Lyle Ungar Les Valiant Marco Wiering Mary-Anne Williams David Wolpert University of Pennsylvania, USA Harvard University, USA University of Groningen, The Netherlands University of Technology, Sydney, Australia NASA, USA Organizing Committee Tsvi Achler Eric Baum Sarah Bull Ben Goertzel Marcus Hutter Emanuel Kitzelmann David Orban Stephen Reed University of Illinois at Urbana Champaign, USA Azure Sky Research Inc., USA NICTA, Australia Novamente LLC, USA Australian National University, Australia University of Bamberg, Germany Singularity University, USA Texai.org, USA Local Organizing Committee Carlo Lepori Mauro Pezze Jürgen Schmidhuber Albino Zgraggen CFO of IDSIA Dean of the Faculty of Informatics of USI IDSIA CFO of the University of Lugano/USI External Reviewers Subhadip Bandyopadhyay Arijit Laha Srinivas Narasimhamurthy Bintu Vasudevan
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 12 and 13: Table of Contents Full Articles. Ef
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
Page 73 and 74:
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 )∗∗
Page 89 and 90:
size of the grid grows. Proposition
Page 91 and 92:
Algorithm 2 Propagate Procedure Pro
Page 93 and 94:
#relations MiniMaxSAT DPLL-S 5 0.9s
Page 95 and 96:
is useful for designing the perform
Page 97 and 98:
its knowledge is limited, and even
Page 99 and 100:
RISC vs. CISC trade-offs in traditi
Page 101 and 102:
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
Page 111 and 112:
we can substitute δ t e t , based
Page 113 and 114:
LP1 Sensing a world state world_sta
Page 115 and 116:
given observed face was considered
Page 117 and 118:
analysis (verification) as has been
Page 119 and 120:
Core Modules Five core regions in t
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agent. These modules receive instru
Page 123 and 124:
The image processing done to extrac
Page 125 and 126:
An agent-environment perception is
Page 127 and 128:
for only one type of the sub-events
Page 129 and 130:
where c ′ is the confidence of th
Page 131 and 132:
sirability of events, i.e. such tha
Page 133 and 134:
where r G , r P and r Q are the rew
Page 135 and 136:
The rest of the argument parallels
Page 137 and 138:
probability distribution Pr are onl
Page 139 and 140:
Figure 1: (a-b) Two causal networks
Page 141 and 142:
2.5 2.5 2 2 d(t) [bits] 1.5 1 d(t)
Page 143 and 144:
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
Page 149 and 150:
was poorly aligned to human intelli
Page 151 and 152:
claim that the goals of AGI are out
Page 153 and 154:
feedback connections, pages 95-133.
Page 155 and 156:
A non-universal variant (WS96) is r
Page 157 and 158:
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,
Page 163 and 164:
A runtime-dependent performance mea
Page 165 and 166:
A. N. Kolmogorov. Three approaches
Page 167 and 168:
describable regularity in a batch o
Page 169 and 170:
start out with problems that are in
Page 171 and 172:
This CJS estimate makes it easy to
Page 173 and 174:
Frontier Search Sun Yi, Tobias Glas
Page 175 and 176:
program i execution time τ steps i
Page 177 and 178:
Example 12. Consider the criterion
Page 179 and 180:
The Evaluation of AGI Systems Pei W
Page 181 and 182:
telligence, the evaluation needs to
Page 183 and 184:
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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