Abstract
There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair 〈Φ,α〉 where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. Different logics provide different definitions for consistency and entailment and hence give us different options for argumentation. An appealing option is classical first-order logic which can express much more complex knowledge than possible with defeasible or classical propositional logics. However the computational viability of using classical first-order logic is an issue. Here we address this issue by using the notion of a connection graph and resolution with unification. We provide a theoretical framework and algorithm for this, together with some theoretical results.
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
Amgoud, L., Cayrol, C.: A model of reasoning based on the production of acceptable arguments. Annals of Math. and A.I. 34, 197–216 (2002)
Benferhat, S., Dubois, D., Prade, H.: Argumentative inference in uncertain and inconsistent knowledge bases. In: Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1993), pp. 1449–1445 (1993)
Besnard, Ph., Hunter, A.: A logic-based theory of deductive arguments. Artificial Intelligence 128, 203–235 (2001)
Besnard, P., Hunter, A.: Practical first-order argumentation. In: Proceedings of the 20th American National Conference on Artificial Intelligence (AAAI 2005), pp. 590–595. MIT Press, Cambridge (2005)
Besnard, Ph., Hunter, A.: Elements of Argumentation. MIT Press, Cambridge (2008)
Chesñevar, C., Maguitman, A., Loui, R.: Logical models of argument. ACM Computing Surveys 32, 337–383 (2000)
Dung, P., Kowalski, R., Toni, F.: Dialectical proof procedures for assumption-based admissible argumentation. Artificial Intelligence 170, 114–159 (2006)
Efstathiou, V., Hunter, A.: Algorithms for effective argumentation in classical propositional logic: A connection graph approach. In: Hartmann, S., Kern-Isberner, G. (eds.) FoIKS 2008. LNCS, vol. 4932, pp. 272–290. Springer, Heidelberg (2008)
Elvang-Gøransson, M., Krause, P., Fox, J.: Dialectic reasoning with classically inconsistent information. In: Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (UAI 1993), pp. 114–121. Morgan Kaufmann, San Francisco (1993)
García, A., Simari, G.: Defeasible logic programming: An argumentative approach. Theory and Practice of Logic Programming 4(1), 95–138 (2004)
Kowalski, R.: A proof procedure using connection graphs. Journal of the ACM 22, 572–595 (1975)
Kowalski, R.: Logic for problem solving. North-Holland Publishing, Amsterdam (1979)
Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. Journal of Applied Non-Classical Logics 7, 25–75 (1997)
Prakken, H., Vreeswijk, G.: Logical systems for defeasible argumentation. In: Gabbay, D. (ed.) Handbook of Philosophical Logic. Kluwer, Dordrecht (2000)
Robinson, J.A.: A machine-oriented logic based on the resolution principle. J. ACM 12(1), 23–41 (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Efstathiou, V., Hunter, A. (2009). An Algorithm for Generating Arguments in Classical Predicate Logic. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-02906-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02905-9
Online ISBN: 978-3-642-02906-6
eBook Packages: Computer ScienceComputer Science (R0)