Neural Network Models of Conditional

Leitgeb, Hannes (25. Oktober 2018): Neural Network Models of Conditional. In: Hansson, Sven Ove und Hendricks, Vincent F. (Hrsg.): Introduction to Formal Philosophy. Springer Undergraduate Texts in Philosophy, Berlin: Springer. S. 147-176

Abstract

This chapter explains how artificial neural networks may be used as models for reasoning, conditionals, and conditional logic. It starts with the historical overlap between neural network research and logic, it discusses connectionism as a paradigm in cognitive science that opposes the traditional paradigm of symbolic computationalism, it mentions some recent accounts of how logic and neural networks may be combined, and it ends with a couple of open questions concerning the future of this area of research.

• Dokumententyp: Buchbeitrag
• Fakultät: Philosophie, Wissenschaftstheorie und Religionswissenschaft > Munich Center for Mathematical Philosophy (MCMP) ; Philosophie, Wissenschaftstheorie und Religionswissenschaft > Lehrstuhl für Logik und Sprachphilosophie
• Themengebiete: 100 Philosophie und Psychologie > 100 Philosophie ; 100 Philosophie und Psychologie > 160 Logik
• ISBN: 978-3-319-77433-6
• Ort: Berlin
• Sprache: Englisch

Literaturliste

Adams, E. (1975). The logic of conditionals: An application of probability to deductive logic (Synthese library, Vol. 86). Dordrecht: Reidel.

Adams, E. (1998). A primer of probability logic (CSLI lecture notes). Stanford: Center for the study of language and information

Bader, S., & Hitzler, P. (2005). Dimensions of neural-symbolic integration – a structured survey. In S. N. Artemov, H. Barringer, A. S. d’Avila Garcez, L. C. Lamb, & J. Woods (Eds.), We will show them! Essays in honour of Dov Gabbay (Federation for computational logic, pp. 167–194). London: College Publications, Int.

Balkenius, C., & Gärdenfors, P. (1991). Nonmonotonic inferences in neural networks. In J. A. Allen, R. Fikes, & E. Sandewall (Eds.), Principles of knowledge representation and reasoning (pp. 32–9). San Mateo: Morgan Kaufmann.

Bechtel, W. (1994). Natural deduction in connectionist systems. Synthese, 101(3), 433–463.

Bechtel, W. (1996). What should a connectionist philosophy of science look like? In R. M. McCauley (Ed.), The Churchlands and their critics (pp. 121–44). Massachusetts: Basil Blackwell.

Bennett, J. (2003). A philosophical guide to conditionals. Oxford: Clarendon Press.

Bird, A. (2002). What is in a paradigm? Richmond Journal of Philosophy, 1(ii), 11–20.

Blutner, R. (2004). Nonmonotonic inferences and neural networks. Synthese, 142, 143–74.

Boutsinas, B., & Vrahatis, M. N. (2001). Artificial nonmonotonic neural networks. Artificial Intelligence, 132, 1–38.

Brewka, G., Dix, J., & Konolige, K. (1997). Nonmonotonic reasoning. An overview (CSLI lecture notes, Vol. 73). Stanford: CSLI Publications.

Chalmers, D. J. (1990). Syntactic transformations on distributed representations. Connection Science, 2(1 & 2), 53–62.

Chen, C. -H., & Honavar, V. (1999). A neural-network architecture for syntax analysis. IEEE Transactions on Neural Networks, 10(1), 94–114.

Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. London: MIT Press.

d’Avila Garcez, A. S., Broda, K., & Gabbay D. M. (2001). Symbolic knowledge extraction from trained neural networks: A sound approach. Artificial Intelligence, 125, 153–205.

d’Avila Garcez, A. S., Broda, K. B., & Gabbay D. M. (2002). Neural-symbolic learning systems. London: Springer.

d’Avila Garcez, A. S., Lamb, L. C., & Gabbay, D. M. (2009). Neural-symbolic cognitive reasoning. Berlin: Springer.

d’Avila Garcez, A. S., Besold, T. R., de Raedt, L., Földiak, P., Hitzler, P., Icard, T., Kühnberger, K. -U., Lamb, L. C., Miikkulainen, R., & Silver, D. L. (2015). Neural-symbolic learning and reasoning: Contributions and challenges. Paper presented at the 2015 AAAI spring symposium series, Stanford.

Dietz, E. -A., Hölldobler, S., Palacios, L. (2015). A connectionist network for skeptical abduction. Paper presented at NeSy 2015, Buenos Aires.

Flach, P. A. (2000). Logical characterizations of inductive learning. In D. M. Gabbay & R. Kruse (Eds.), Handbook of defeasible reasoning and uncertainty management systems (Vol. 4, pp. 155–96). Dordrecht: Kluwer.

Fodor, J., & Pylyshyn, Z. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3–71.

Gärdenfors, P. (1988). Knowledge in flux. Cambridge, MA: The MIT Press.

Gärdenfors, P. (1994). How logic emerges from the dynamics of information. In J. Van Eijck & A. Visser (Eds.), Logic and information flow (pp. 49–77). Cambridge: The MIT Press.

Ginsberg, M. L. (Ed.). (1987). Readings in nonmonotonic reasoning (pp. 1–23). Los Altos: Morgan Kaufmann.

Hansson, S. O. (1999). A textbook of belief dynamics. Dordrecht: Kluwer.

Hitzler, P., Hölldobler, S., & Seda, A. K. (2004). Logic programs and connectionist networks. Journal of Applied Logic, 2(3), 245–272.

Hölldobler, S. (1993). Automated inferencing and connectionist models (Post-doctoral thesis).

Hölldobler, S., & Kalinke, Y. (1994). Towards a massively parallel computational model for logic programming. In Proceedings ECAI94 Workshop on Combining Symbolic and Connectionist Processing (pp. 68–77). ECCAI.

Hölldobler, S. (2009). Cognitive science, computational logic and connectionism. In M. Adriani, et al. (Eds.), Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 1–6).

Horgan, T., & Tienson, J. (1996). Connectionism and the philosophy of psychology. Cambridge: The MIT Press.

Kleene, S. C. (1956). Representation of events in nerve nets and finite automata. In C. E. Shannon & J. McCarthy (Eds.), Automata studies (pp. 3–42). Princeton: Princeton University Press.

Kraus, S., Lehmann, D., & Magidor, M. (1990). Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44, 167–207.

Legendre, G., Miyata, Y., & Smolensky, P. (1994). Principles for an integrated connectionist/symbolic theory of higher cognition. Hillsdale: L. Erlbaum.

Lehmann, D., & Magidor, M. (1992). What does a conditional knowledge base entail? Artificial Intelligence, 55, 1–60.

Leitgeb, H. (2001). Nonmonotonic reasoning by inhibition nets. Artificial Intelligence, 128(1–2), 161–201.

Leitgeb, H. (2003). Nonmonotonic reasoning by inhibition nets II. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(suppl., issue 2), 105–35.

Leitgeb, H. (2004). Inference on the low level. An investigation into deduction, nonmonotonic reasoning, and the philosophy of cognition (Applied logic series). Dordrecht: Kluwer/Springer.

Leitgeb, H. (2005). Interpreted dynamical systems and qualitative laws: From inhibition networks to evolutionary systems. Synthese, 146, 189–202.

Leitgeb, H. (2005). Reseaux de neurones capables de raisonner. Dossier Pour la Science (Special issue of the French edition of the Scientific American) October/December, 97–101.

Leitgeb, H. (2007). Beliefs in conditionals vs. conditional beliefs. Topoi, 26(1), 115–32.

Leitgeb, H. (2007) Neural network models of conditionals: An introduction. In X. Arrazola, J. M. Larrazabal, et al. (Eds.), LogKCA-07, Proceedings of the First ILCLI International Workshop on Logic and Philosophy of Knowledge, Communication and Action (pp. 191–223). Bilbao: University of the Basque Country Press.

Levesque, H. (1990). All I know: A study in autoepistemic logic. Artificial Intelligence, 42, 263–309.

Lewis, D. (1973). Counterfactuals. Oxford: Blackwell.

Makinson, D. (1994). General patterns in nonmonotonic reasoning. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.), Handbook of logic in artificial intelligence and logic programming (Vol. 3, pp. 35–110.). Oxford: Clarendon Press.

McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–33; Reprinted in: W. S. McCulloch, Embodiments of mind. Cambridge, MA: The MIT Press (1965).

Ortner, R., & H. Leitgeb (2011). Mechanizing induction. In D. Gabbay, S. Hartmann, & J. Woods (Eds.), Handbook of the history of logic. Volume 10: Inductive logic (pp. 719–772). Oxford: Elsevier.

Peters, S., & Westerstahl, D. (2006). Quantifiers in language and logic. Oxford: Oxford University Press.

Pinkas, G. (1991). Symmetric neural networks and logic satisfiability. Neural Computation, 3, 282–291.

Pinkas, G. (1995). Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge. Artificial Intelligence, 77, 203–247.

Rojas, R. (1996). Neural networks – a systematic introduction. Berlin: Springer.

Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (1986). Parallel distributed processing (Vols. 1 and 2). Cambridge: The MIT Press.

Schurz, G. (2002). Ceteris paribus laws: Classification and deconstruction. Erkenntnis, 57(3), 351–72.

Schurz, G., & Leitgeb, H. (Eds.). (2005). Special volume on “Non-monotonic and uncertain reasoning in the focus of paradigms of cognition. Synthese, 146, 1–2.

Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–23.

Smolensky, P. (1990). Tensor-product variable binding and the representation of symbolic structures in connectionist networks. Artificial Intelligence, 46, 159–216.

Smolensky, P., & Legendre, G. (2006). The harmonic mind: From neural computation to optimality-theoretic grammar. Cambridge, MA: The MIT Press.

Stenning, K., & Lambalgen, M. van (2008). Human reasoning and cognitive science. Cambridge: The MIT Press.

van Benthem, J. (1984). Foundations of conditional logic. Journal of Philosophical Logic, 13(3), 303–49.

Van Gelder, T. J. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21, 615–65.

Van Gelder, T. J. (1999). Distributed versus local representation. In R. Wilson & F. Keil (Eds.), The MIT encyclopedia of cognitive sciences (pp. 236–38). Cambridge: The MIT Press.

Wheeler, G. (2017). Machine epistemology and big data. In L. McIntyre & A. Rosenberg (Eds.), The Routledge companion to philosophy of social science (pp. 321–329). New York: Routledge.