Advances in Intelligent Systems Research - of Marcus Hutter

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arguably, these machines are, according to our future selves, more valuable to the community than we are since they are more likely to act in the best interests of the community. Clearly they must be considered part of the community and we must be fair to them in order to achieve the greatest good effect – and yet, this is likely to be the most difficult and time-consuming step of all. It is also worthwhile to note that all of the things recommended for machines are just good ethical hygiene for humans as well. References Asimov, I. 1942. Runaround. Astounding Science Fiction March 1942. New York, NY: Street & Smith. Baars, B.J. 1997. In The Theater of Consciousness: The Workspace of the Mind. New York, New York: Oxford University Press. Baum, E. 2006. What Is Thought? MIT Press. Brosnan, S. and de Wall, F. 2003. Monkeys reject unequal pay. Nature 425: 297-299. Clarke, R. 1993. Asimov’s Laws of Robotics: Implications for Information Technology, Part I. IEEE Computer 26(12):53-61. Clarke, R. 1994. Asimov’s Laws of Robotics: Implications for Information Technology, Part II. IEEE Computer 27(1):57-66. Hauser, M.; Chen, K.; Chen, F.; and Chuang, E. 2003. Give unto others: genetically unrelated cotton-top tamarin monkeys preferentially give food to those who give food back. In Proceedings of the Royal Society, London, B 270: 2363-2370. London, England: The Royal Society. Hauser, M. 2006. Moral Minds: How Nature Designed Our Universal Sense of Right and Wrong. New York, NY: HarperCollins/Ecco. Hauser, M. et al. 2007. A Dissociation Between Moral Judgments and Justifications. Mind&Language 22(1):1-27. Minsky, M. 2006. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York, NY: Simon & Schuster. Omohundro, S. M. 2008a. The Basic AI Drives. In Proceedings of the First Conference on Artificial General Intelligence, 483-492. Amsterdam: IOS Press. Omohundro, S. M. 2008b. The Nature of Self-Improving Artificial Intelligence. Available at http://selfawaresystems .files.wordpress.com Ohman, A.; Flykt, A.; and Esteves, F. 2001. Emotion Drives Attention: Detecting the Snake in the Grass. Journal of Experimental Psychology: General 130(3): 466- 478. Perlis, D. 2008. To BICA and Beyond: RAH-RAH-RAH! –or– How Biology and Anomalies Together Contribute to Flexible Cognition. In AAAI Technical Report FS-08-04. Menlo Park, CA: AAAI Press. Range, F.; Horn, L.; Viranyi, Z.; and Huber, L. 2008. The absence of reward induces inequity inversion in dogs. Proceedings of the National Academy of Sciences USA 2008 : 0810957105v1-pnas.0810957105. Sloman, A. 1999. What Sort of Architecture is Required for a Human-like Agent? In Wooldridge, M. and Rao, A.S. eds Foundations of Rational Agency. Dordrecht, Netherlands: Kluwer Academic Publishers. Soon, C.S.; Brass, M.; Heinze, H-J; and Haynes, J-D. 2008. Unconscious determinants of free decisions in the human brain. Nature Neuroscience 11: 543-545. Tomasello, M. 2009. Why We Cooperate. MIT Press. Trivers, R. 1991. Deceit and self-deception: The relationship between communication and consciousness. In Robinson, M and Tiger, L. eds. Man and Beast Revisited. Washington, DC: Smithsonian Press. de Waal, F. 2009. The Age of Empathy: Nature’s Lessons for a Kinder Society. New York, NY: Harmony Books/Random House. de Waal, F. 2006. Primates and Philosophers: How Morality Evolved. Princeton University Press. Waser, M. 2008. Discovering The Foundations Of A Universal System Of Ethics As A Road To Safe Artificial Intelligence. In AAAI Technical Report FS-08-04. Menlo Park, CA: AAAI Press. Waser, M. 2009. A Safe Ethical System for Intelligent Machines. In AAAI Technical Report FS-09-01. Menlo Park, CA: AAAI Press. Wilson, J. 1993. The Moral Sense. New York: Free Press. Yudkowsky, E. 2001. Creating Friendly AI 1.0: The Analysis and Design of Benevolent Goal Architectures. Available at http://singinst.org/CFAI.html. Yudkowsky, E. 2004. Coherent Extrapolated Volition. Available at http://www.singinst.org/upload/CEV.html. 175
Software Design of an AGI System Based on Perception Loop Antonio Chella Dip. di Ingegneria Informatica University of Palermo chella@dinfo.unipa.it Massimo Cossentino ICAR-CNR Consiglio Nazionale delle Ricerche cossentino@pa.icar.cnr.it Valeria Seidita Dip. di Ingegneria Informatica University of Palermo seidita@dinfo.unipa.it Abstract According to the externalist approach, subjective experience hypothesizes a processual unity between the activity in the brain and the perceived event in the external world. A perception loop therefore occurs among the brain’s activities and the external world. In our work the metaphor of test is employed to create a software design methodology for implementing an AGI system endowed with the perception loop. Preliminary experiments with a NAO humanoid robots are reported. Position Statement Machine consciousness concerns the attempt to model and implement in a robot those aspects of human cognition which are identified with the controversial phenomenon of consciousness, see (CM09) for a review. It does not necessarily try to reproduce human consciousness as such, insofar as human consciousness could be unique due to a complex series of cultural, social, and biological conditions. However, many authors suggested one or more specific aspects and functions of consciousness that could, at least in principle, be replicated in a robot. It should be remembered that at the beginning of the information era there was no clear cut separation between intelligence and consciousness. Both were considered vaguely overlapping terms referring to what the mind was capable of. There was no clear boundary between intelligence and consciousness. Today, machine consciousness assumes that there is some aspect of the mind that has not yet been adequately addressed. Therefore, scholars in the field suspect that there could be something more going on in the mind than what is currently under scrutiny in field such as artificial intelligence, cognitive science, and computer science. AGI researchers would agree that there is still a lot of work to do: better algorithms, more data, more complex, and faster learning structures. However it could be doubted whether these improvements in AGI would ever lead to an artificial agent equivalent to a biological mind or it would rather miss some necessary aspect. We, among others, suspect that classic AI missed something important; put it more clearly, we claim that facing consciousness, and in particular the subjective experience, is an essential requirement for an AGI based artifact, see, e.g. (Goe06). In this paper we move from the externalist oriented point of view (Man06), according to which subjective experience supposes a processual unity between the activity in the brain and the perceived event in the external world. According to externalism, the separation between subject and object must be reconsidered so that the two, while maintaining their identities as different perspectives on a process, actually occur as a unity during perception. Starting from these considerations, we developed robots aiming to exploit sensorimotor contingencies and externalist inspired frameworks (Che07). An interesting architectural feature is the implementation of a generalized perception loop based on the perceptual space as a whole. In other words, in classic feedback only a few parameters are used to control robot behavior (position, speed, etc.). The idea behind our robots is to match a global prediction of the future perceptual state (for instance by a rendering of the visual image) with the incoming data. The goal is thus to achieve a tight coupling between robot and environment. According to these model and implementations, the physical correlate of robot subjective experience would not lie in the images internally generated but rather in the causal processes engaged between the robot and the environment. We developed a software design methodology for implementing the generalized perception loop in an AGI system. The PASSI agent oriented methodology (Cos05) has been taken into account as the starting point (CCS09) for this new design process. Until now PASSI has been used, and proved to be useful in that, for engineering robotic applications. In this work the metaphor of test is employed thus providing means for designing the elements of the perception loop. We reused, modified and integrated two process fragments coming from the Unfied Process (UP) Test Plan and Design and Test Execution, the former’s aim is to identify the system functionalities to be tested, the available system resources and the test objective. The latter aims at executing test in order to identify defects and analyze 176
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Eric Baum, Marcus Hutter, Emanuel K
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In Memoriam Ray Solomonoff (1926-20
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Artificial General Intelligence Vol
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Conference Organization Chairs Marc
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Table of Contents Full Articles. Ef
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Uncertain Spatiotemporal Logic for
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inference. Constraint graphs compac
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s ← the rule system‟s opinion o
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Run Time (sec) Run Time (sec) probl
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ut also, more importantly, by the c
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pattern recognition only, while at
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Central would be a two-way interact
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set of the OpenCogPrime architectur
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mentioned elements to the real elem
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Suppose it has previously been show
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man reality; we have given a semi-f
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t∑ Vµ,g,T π ≡ E( r g (I g,s,i
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as we have formalized it here is sp
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s1 s2 s3 s4 s5 s6 s7 1 0 1 0 1 0 1
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valued for every τ and this value
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Environment Type General Bounded Ba
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The sliding window is passed over t
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data. However, TP alone performs ve
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Extension to Non-Symbolic Data Stri
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agent’s uncertain reasoning, than
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Theorem 2. Suppose that in addition
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List lnheritance $E $C Inheritance
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The Toy Box Problem As with existin
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the conceptual mismatch between the
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Initial 2D World State Impact in 2D
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R Rewriting Rule: a b a R b a b b
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ements connected by binary row and
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White uses E Black uses E Gomoku 78
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Approach We have used a NARMAX appr
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Range [cm] Range [cm] as well as th
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system with the computed rotational
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(a) (b) Figure 1: DCT network repre
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(a) Pole balancing (b) T-maze (c) B
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lated weights, i.e. requiring the f
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In this paper, we will discuss heur
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Consider again a substitution θ as
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(Ax S ) ∗∗ (Ax + j S S )∗∗
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size of the grid grows. Proposition
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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
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10 5 0 −5 −10 20 40 60 80 100 1
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References [Bas06] A. J. Bastian. L
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Our algorithm incorporates gradient
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is off-policy λ-return and ¯φ t
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we can substitute δ t e t , based
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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
Page 139 and 140: Figure 1: (a-b) Two causal networks
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Page 143 and 144: eak this clique and then learning i
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Page 151 and 152: claim that the goals of AGI are out
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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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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
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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
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Page 177 and 178: Example 12. Consider the criterion
Page 179 and 180: The Evaluation of AGI Systems Pei W
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Page 183 and 184: Now we see that the empirical appro
Page 185 and 186: Designing a Safe Motivational Syste
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Page 189: architecture based upon Sloman’s
Page 193 and 194: A Theoretical Framework to Formaliz
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Page 201 and 202: Compression Progress, Pseudorandomn
Page 203 and 204: Relational Local Iterative Compress
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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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