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Titlebook: Artificial General Intelligence; 5th International Co Joscha Bach,Ben Goertzel,Matthew Iklé Conference proceedings 2012 Springer-Verlag Ber

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CHREST Models of Implicit Learning and Board Game Interpretation,rning, checking the validity of strings drawn from an artificial grammar. The second looks at the interpretation of boardgame positions. The same learning and retrieval mechanisms are used in both cases, and we argue that CHREST can be used by an artificial general intelligence to construct and access declarative memory.
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P. C. Perdikaris,A. M. Calomino of cost for partial metric spaces. As examples we consider the cost of computing a double negation ¬¬. in two-valued propositional logic, the cost of computing . in logic programming, and a cost model for the . time delay.
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https://doi.org/10.1007/978-3-642-35506-6algorithmic probability; computational theory; graphical models; reinforcement learning; universal agent
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https://doi.org/10.1007/978-3-319-32634-4o distinguish human-level intelligence from other forms of intelligence (be it natural or artificial). We claim that creativity can be reduced in many interesting cases to cognitive mechanisms like analogy-making and concept blending. These mechanisms can best be modeled using (non-classical) logica
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https://doi.org/10.1007/978-94-009-6149-4form fetch-and-carry tasks, potentially for use in an eldercare context. By “extensible” we mean that the robot is able to learn new nouns and verbs by simple interaction with its user. An associated video [1] illustrates the range of phenomena handled by our implemented real-time system.
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https://doi.org/10.1007/978-94-009-6152-4resent a representation theorem governing the integration of causal models with decision theory. This theorem puts formal bounds on the applicability of the ., a normative theory of decision counterfactuals that has previously been argued on . and practical grounds, as well as by comparison to theor
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