Advances in Intelligent Systems Research - of Marcus Hutter

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eing the maternal input used for training. Compared to other approaches in the field (e.g., connectionism), the extent of MOSAIC’s coverage is striking, in particular when one considers that only simple and local learning mechanisms are used. In a nutshell, three interacting factors are essential for explaining MOSAIC’s behaviour: rote learning, the creation and use of generative links, and the statistical structure of the input. General Intelligence: Some Central Questions Although CHREST does not yet show fully general intelligence, it offers a cognitive architecture whose combined components have proved sufficient to exhibit behaviour that is, in several domains, remarkably close to human behaviour. In this final section, we explore some central questions in artificial intelligence and cognitive science for which, we believe, CHREST provides important insight. The Role of Concepts It is generally accepted that acquiring and using concepts is essential for the survival of organisms and their successful interaction with the environment. Without concepts, organisms can show only primitive behaviour, as the lack of generalization means that each new exemplar must be treated separately. A considerable amount of experimental research has been carried out on concept formation and categorization in psychology over the last decades. We know a great deal about how people acquire and utilise concepts, with a number of computational models accounting for different aspects of these empirical data. For example, Gobet et al. (1997) used a model close to CHREST to investigate the role of strategies in concept formation, and the suitability of CHREST for simulating categorization experiments was further established by Lane and Gobet (2005). The previous simulations with CHREST have essentially relied on individual nodes to account for the concepts used by humans. However, the type of chunking networks created by CHREST suggests other possibilities as well. A natural interpretation of these networks is that concepts do not map into single chunks (or even single templates), but rather correspond to a subset of nodes interlinked, to varying degrees, by lateral links. In this view, concepts are much more distributed than in standard symbolic models, and take on some of the flavour of how concepts are represented in connectionist networks; this idea is expanded upon in Lane, Gobet and Cheng (2000). While this idea is not new, the advantage of using CHREST is to provide mechanisms explaining how nodes and the links between them are acquired autonomously and in real time, how they relate to perceptual input, how they are influenced by the structure of the environment, and how they may integrate information across different modalities. Embodiment Although the first experiments with autonomous robots are fairly old, going back to Grey Walter’s (1953) seminal work, it is only in the last twenty years or so that the field of embodied cognition has been taken up. A number of mobile robots have been created that are able to carry out fairly simple tasks (Pfeifer and Scheier, 1999). However, as we have argued elsewhere (Lane and Gobet, 2001), a limitation of current research in this field is that it does not provide mechanisms for how simple behaviours can be linked with more complex behaviours. The limitation is serious, as it is obvious that systems showing general intelligence must have (at least) these two levels of complexity. Specifically, the lack of symbolic processing in current embodied-cognition systems means that there are important limits in the kind of behaviours that can be addressed. As soon as one tries to link simple behaviours with symbolic processes, the question of symbol grounding arises. The CHREST framework provides a fairly simple explanation for this: as noted above, symbols, that is chunks, are grounded through perception. Importantly, as mentioned above, this idea goes much beyond that of simply linking symbols to external objects, through perception. Chunks also shape how the world is perceived, in two important ways. First, they determine how percepts will be organized. The way former world chess champion Gary Kasparov perceptually groups information of a chess position is different from the way an amateur does. Second, chunks actively direct eye movements and thus determine to which part of the display attention will be heeded. To show how such an integrated system would work, we are currently working to implement CHREST into a mobile robot. Our first aim is to show that a chunking-based architecture can replicate some of the 'classic' simulations in the literature, such as the avoidance of obstacles and the emergence of complex behaviour. Our belief is that combining symbolic and non-symbolic approaches within an embodied system is likely to have important consequences, both for theory and application. In particular, symbolic information will enable the robot to be 'articulate', explaining, verbally, what and why it takes particular decisions. Conclusion We have argued that progress towards understanding general intelligence requires an integrated approach: not just integrating perception with learning, but also integrating high-level and low-level modes of processing. It is unlikely that a satisfactory theory of human intelligence will be developed from a single explanatory framework, so we expect an artificial system with claims to general intelligence to be a unification of diverse approaches. In this paper, we have set out the case for a symbolic system, which integrates perception with learning. We have shown how the system captures many details of high-level processing in human cognition, and also how it captures physical behaviours, such as details of eye fixations. These successes allow us to propose some ideas of how a complete model of the mind may look. 11
Central would be a two-way interaction between perception and cognition. This interaction must be coupled with an incremental learning system, capable of acquiring a vast and coherent structure of nodes and links. But also, paradoxically perhaps, the architecture should exhibit strong constraints, such as limited processing time or short-term memory capacities. These limits lie behind some of the key empirical challenges to computational theories of psychological behaviour. Acknowledgements This research was supported by the Economics and Social Research Council under grant number RES-000-23-1601. References Anderson, J. R., and Lebière, C. (Eds.). (1998). The atomic components of thought. Mahwah, NJ: Erlbaum. Chase, W. G., and Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55-81. De Groot, A. D., and Gobet, F. (1996). Perception and memory in chess: Heuristics of the professional eye. Assen: Van Gorcum. Dreyfus, H., and Dreyfus, S. (1986). Mind over machine. New York: Free Press. Feigenbaum, E. A., and Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8, 305-336. Freudenthal, D., Pine, J. M., Aguado-Orea, J. & Gobet, F. (2007). Modelling the developmental patterning of finiteness marking in English, Dutch, German and Spanish using MOSAIC. Cognitive Science, 31, 311-341. Freudenthal, D., Pine, J. M., & Gobet, F. (2009). Simulating the referential properties of Dutch, German and English Root Infinitives in MOSAIC. Language Learning and Development, 5, 1-29. Gobet, F. (1997). A pattern-recognition theory of search in expert problem solving. Thinking and Reasoning, 3, 291-313. Gobet, F. (2009). Using a cognitive architecture for addressing the question of cognitive universals in crosscultural psychology: The example of awalé. Journal of Cross-Cultural Psychology, 40, 627-648. Gobet, F., and Jansen, P. (1994). Towards a chess program based on a model of human memory. In H. J. van den Herik, I. S. Herschberg and J. E. Uiterwijk (Eds.), Advances in Computer Chess 7 (pp. 35-60). Maastricht: University of Limburg Press. Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C.-H., Jones, G., Oliver, I., and Pine, J. M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences, 5, 236-243. Gobet, F., Richman, H., Staszewski, J., and Simon, H. A. (1997). Goals, representations, and strategies in a concept attainment task: The EPAM model. The Psychology of Learning and Motivation, 37, 265-290. Gobet, F., and Simon, H. A. (1996). Recall of rapidly presented random chess positions is a function of skill. Psychonomic Bulletin and Review, 3, 159-163. Gobet, F., and Simon, H. A. (2000). Five seconds or sixty? Presentation time in expert memory. Cognitive Science, 24, 651-682. Gobet, F., and Waters, A. J. (2003). The role of constraints in expert memory. Journal of Experimental Psychology: Learning, Memory and Cognition, 29, 1082-1094. Grey Walter, W. The living brain (1953). London, UK: Penguin. Han, J.W., Lane, P.C.R., Davey, N. and Sun Y. (2009). Attention mechanisms and component-based face detection. Proceedings of the International Conference on Methods and Models in Computer Science (IEEE Computer Society). Jones, G., Gobet, F., & Pine, J. M. (2007). Linking working memory and long-term memory: A computational model of the learning of new words. Developmental Science, 10, 853-873. Jones, G., Gobet, F., & Pine, J. M. (2008). Computer simulations of developmental change: The contributions of working memory capacity and long-term knowledge. Cognitive Science,32, 1148-1176. Lane, P. C. R., Cheng, P. C.-H., and Gobet, F. (2000). CHREST+: Investigating how humans learn to solve problems using diagrams. AISB Quarterly, 103, 24-30. Lane, P. C. R., and Gobet, F. (2001). Simple environments fail as illustrations of intelligence: A review of R. Pfeifer and C. Scheier: 'Understanding Intelligence'. Artificial Intelligence, 127, 261-267. Lane, P. C. R., & Gobet, F. (2005). Discovering predictive variables when evolving cognitive models. Third International Conference on Advances in Pattern Recognition. Lane, P.C.R., Gobet, F. & Cheng, P.C-H. (2000). Learningbased constraints on schemata. Proceedings of the Twenty- Second Annual Conference of the Cognitive Science Society, pp. 776-81. Lane, P. C. R., Sykes, A. K., and Gobet, F. (2003). Combining low-level perception with expectations in CHREST. In F. Schmalhofer, R. M. Young and G. Katz (Eds.), Proceedings of EuroCogSci 03: The European Cognitive Science Conference 2003 (pp. 205-210). Mahwah, NJ: Erlbaum. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press. Pfeifer, R., and Scheier, C. (1999). Understanding intelligence. Cambridge: MIT Press. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behaviour, 6, 855-863. Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA: MIT Press. Simon, H. A. (1979). Models of thought (Vol. 1). New Haven, CT: Yale University Press. Simon, H. A., and Gilmartin, K. J. (1973). A simulation of memory for chess positions. Cognitive Psychology, 5, 29-46. 12
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 12 and 13: Table of Contents Full Articles. Ef
Page 14: Uncertain Spatiotemporal Logic for
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Page 25: pattern recognition only, while at
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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
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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
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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
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Page 71 and 72: Approach We have used a NARMAX appr
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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
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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
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The position of the image plane at
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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
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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
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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
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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
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telligence, the evaluation needs to
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Now we see that the empirical appro
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Designing a Safe Motivational Syste
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non-problematic result than explora
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architecture based upon Sloman’s
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Software Design of an AGI System Ba
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A Theoretical Framework to Formaliz
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Uncertain Spatiotemporal Logic for
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A (hopefully) Unbiased Universal En
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Neuroethological Approach to Unders
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Compression Progress, Pseudorandomn
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Relational Local Iterative Compress
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Stochastic Grammar Based Incrementa
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Compression-Driven Progress in Scie
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Concept Formation in the Ouroboros
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On Super-Turing Computing Power and
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A minimum relative entropy principl
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Author Index Araujo, Samir . . . .
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