Advances in Intelligent Systems Research - of Marcus Hutter

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Eric Baum, Marcus Hutter, Emanuel Kitzelmann (Editors) Artificial General Intelligence Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010, Lugano, Switzerland, March 5-8, 2010 Amsterdam-Beijing-Paris ISBN: 978-90-78677-36-9
________________________________________________________________________ Advances in Intelligent Systems Research volume 10 ________________________________________________________________________
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 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
Page 81 and 82:
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
Page 121 and 122:
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
Page 159 and 160:
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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