Abstract
A theory of defeasible arguments is proposed that combines logical and probabilistic properties. This logico-probabilistic argumentation theory builds on two foundational theories of nonmonotonic reasoning and uncertainty: the study of nonmonotonic consequence relations (and the associated minimal model semantics) and probability theory. A key result is that, in the theory, qualitatively defined argument validity can be derived from a quantitative interpretation. The theory provides a synthetic perspective of arguments ‘jumping to conclusions’, rules with exceptions, and probabilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baroni, P., Caminada, M., Giacomin, M.: Review: an introduction to argumentation semantics. The Knowledge Engineering Review 26(4), 365–410 (2011)
van Benthem, J.: Foundations of conditional logic. Journal of Philosophical Logic, 303–349 (1984)
Bex, F., Van Koppen, P., Prakken, H., Verheij, B.: A hybrid formal theory of arguments, stories and criminal evidence. Artificial Intelligence and Law, 1–30 (2010)
Dubois, D., Prade, H.: Possibility theory, probability theory and multiple-valued logics: A clarification. Annals of Mathematics and Artificial Intelligence 32(1), 35–66 (2001)
Dung, P.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77, 321–357 (1995)
Geffner, H., Pearl, J.: Conditional entailment: Bridging two approaches to default reasoning. Artificial Intelligence 53(2-3), 209–244 (1992)
Hájek, A.: Interpretations of probability. In: Zalta, E. (ed.) The Stanford Encyclopedia of Philosophy, Stanford University (2011)
Hawthorne, J., Makinson, D.: The quantitative/qualitative watershed for rules of uncertain inference. Studia Logica 86(2), 247–297 (2007)
Jensen, F., Nielsen, T.: Bayesian networks and decision graphs. Springer, Berlin (2007)
Josephson, J., Josephson, S.: Abductive Inference: Computation, Philosophy, Technology. Cambridge University Press, Cambridge (1996)
Kraus, S., Lehmann, D., Magidor, M.: Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence 44, 167–207 (1990)
Makinson, D.: General patterns in nonmonotonic reasoning. In: Gabbay, D., Hogger, C., Robinson, J. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming. Nonmonotonic Reasoning and Uncertain Reasoning, vol. 3, pp. 35–110. Clarendon Press, Oxford (1994)
Pearl, J.: Causality. Cambridge University Press, Cambridge (2000)
Pollock, J.: Cognitive Carpentry: A Blueprint for How to Build a Person. The MIT Press, Cambridge (1995)
Prakken, H.: An abstract framework for argumentation with structured arguments. Argument and Computation 1(2), 93–124 (2010)
Rahwan, I., Simari, G. (eds.): Argumentation in Artificial Intelligence. Springer, Dordrecht (2009)
Verheij, B.: Two approaches to dialectical argumentation: admissible sets and argumentation stages. In: Meyer, J.J., van der Gaag, L. (eds.) Proceedings of NAIC 1996, pp. 357–368. Universiteit Utrecht, Utrecht (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Verheij, B. (2012). Jumping to Conclusions. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds) Logics in Artificial Intelligence. JELIA 2012. Lecture Notes in Computer Science(), vol 7519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33353-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-33353-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33352-1
Online ISBN: 978-3-642-33353-8
eBook Packages: Computer ScienceComputer Science (R0)