Abstract
This paper discusses several key issues in temporal and causal inference in the context of AGI. The main conclusions are: (1) the representation of temporal information should take multiple forms; (2) classical conditioning can be carried out as temporal inference; (3) causal inference can be realized without a predefined causal relation.
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References
Allen, J.F.: Towards a general theory of action and time. Artificial Intelligence 23(2), 123–154 (1984)
Allen, J.F.: Time and time again: The many ways to represent time. International Journal of Intelligent Systems 6(4), 341–356 (1991)
Anderson, J.J., Bracis, C., Goodwin, R.A.: Pavlovian conditioning from a foraging perspective. In: Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1276–1281 (2010)
Cheng, P.W.: From covariation to causation: a causal power theory. Psychological Review 104(2), 367–405 (1997)
Elgot-Drapkin, J., Perlis, D.: Reasoning situated in time I: Basic concepts. Journal of Experimental & Theoretical Artificial Intelligence 2, 75–98 (1990)
Franklin, S.: A foundational architecture for artificial general intelligence. In: Goertzel, B., Wang, P. (eds.) Advance of Artificial General Intelligence, pp. 36–54. IOS Press, Amsterdam (2007)
Franklin, S., Strain, S., McCall, R., Baars, B.: Conceptual commitments of the LIDA model of cognition. Journal of Artificial General Intelligence 4(2), 1–22 (2013)
Gallistel, C.R., Gibbon, J.: Time, rate, and conditioning. Psychological Review 107(2), 289–344 (2000)
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic causal theories. Artificial Intelligence 153, 49–104 (2004)
Goodman, N.D., Ullman, T.D., Tenenbaum, J.B.: Learning a theory of causality. Psychological Review (2011)
Halpern, J.Y., Pearl, J.: Causes and explanations: A structural-model approach. Part I: Causes. The British Journal for the Philosophy of Science 56(4), 843 (2005)
Ismail, H.O., Shapiro, S.C.: Two problems with reasoning and acting in time. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Seventh International Conference, pp. 355–365 (2000)
Kowalski, R.: Logic for Problem Solving. North Holland, New York (1979)
Laird, J.E.: The Soar Cognitive Architecture. MIT Press, Cambridge (2012)
van Lambalgen, M., Hamm, F.: The proper treatment of events. Blackwell Publishing, Malden (2005)
McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence 4, pp. 463–502. Edinburgh University Press, Edinburgh (1969)
Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)
Nivel, E., Thórisson, K.R., Steunebrink, B.R., Dindo, H., Pezzulo, G., Rodriguez, M., Hernandez, C., Ognibene, D., Schmidhuber, J., Sanz, R., Helgason, H.P., Chella, A., Jonsson, G.K.: Bounded recursive self-improvement. CoRR abs/1312.6764 (2013)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Peirce, C.S.: Collected Papers of Charles Sanders Peirce, vol. 2. Harvard University Press, Cambridge (1931)
Piaget, J.: The construction of reality in the child. Basic Books, New York (1954)
Pratt-Hartmann, I.: Temporal prepositions and their logic. Artificial Intelligence 166, 1–36 (2005)
Rattigan, M.J., Maier, M., Jensen, D.: Relational blocking for causal discovery. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)
Rescorla, R., Wagner, A.: A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non reinforcement. In: Black, A., Prokasy, W. (eds.) Classical Conditioning II, pp. 64–99. Appleton-Century-Crofts, New York (1972)
Rosenbloom, P.S.: Rethinking cognitive architecture via graphical models. Cognitive Systems Research 12, 198–209 (2011)
Shoham, Y.: Nonmonotonic reasoning and causation. Cognitive Science 14, 213–252 (1990)
Srivastava, N., Schrater, P.: Classical conditioning via inference over observable situation contexts. In: Proceedings of the 36th Annual Meeting of the Cognitive Science Society, pp. 1503–1508 (2014)
Sutton, R.S., Barto, A.G.: Time-derivative models of Pavlovian reinforcement. In: Gabriel, M., Moore, J. (eds.) Learning and Computational Neuroscience: Foundations of Adaptive Networks, pp. 497–537. MIT Press (1990)
Vila, L.: A survey on temporal reasoning in artificial intelligence. AI Communications 7(1), 4–28 (1994)
Wang, P.: Experience-grounded semantics: a theory for intelligent systems. Cognitive Systems Research 6(4), 282–302 (2005)
Wang, P.: Rigid Flexibility: The Logic of Intelligence. Springer, Dordrecht (2006)
Wang, P.: Non-Axiomatic Logic: A Model of Intelligent Reasoning. World Scientific, Singapore (2013)
Williamson, J.: Causality. In: Gabbay, D., Guenthner, F. (eds.) Handbook of Philosophical Logic, vol. 14, pp. 95–126. Springer (2007)
Xu, Y., Wang, P.: The frame problem, the relevance problem, and a package solution to both. Synthese (2012)
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Wang, P., Hammer, P. (2015). Issues in Temporal and Causal Inference. In: Bieger, J., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science(), vol 9205. Springer, Cham. https://doi.org/10.1007/978-3-319-21365-1_22
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DOI: https://doi.org/10.1007/978-3-319-21365-1_22
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