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Belief Revision and Computational Argumentation: A Critical Comparison

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Abstract

This paper aims at comparing and relating belief revision and argumentation as approaches to model reasoning processes. Referring to some prominent literature references in both fields, we will discuss their (implicit or explicit) assumptions on the modeled processes and hence commonalities and differences in the forms of reasoning they are suitable to deal with. The intended contribution is on one hand assessing the (not fully explored yet) relationships between two lively research fields in the broad area of defeasible reasoning and on the other hand pointing out open issues and potential directions for future research.

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Notes

  1. For the impact of AGM belief revision in the Artificial Intelligence community, see Carnota and Rodríguez (2011).

  2. Here, we are assuming the classical AGM theory (Alchourrón et al. 1985).

  3. For a discussion see also Del Val (1997), Hansson and Olsson (1999).

  4. For details see Makinson (1996), Gärdenfors (2011).

  5. In Belief Change, the postulates that involve minimal change have been the object of controversies. For instance, in contraction, the recovery postulate has been extensively debated (Makinson 1987, 1997a; Hansson 1991, 1999a; Fermé 2001), whereas in the case of revision, the AGM revision operator has been questioned regarding the preservation of minimal change (Rott 2000).

  6. Several different ways for inconsistency-tolerant belief revision can be found in the literature; among them is possible to mention the use of paraconsistent logic instead of classical logic (Priest 2001; Testa et al. 2017, 2018), the use of context sensitivity (Chopra and Parikh 1999; Hansson and Wassermann 2002), and the adoption of belief states (Fermé and Wassermann 2018).

  7. The reader may refer to Baroni and Riveret (2019) for a discussion of the evaluation of statement justification based on argument justification.

  8. The epistemic entrenchment ordering is based on a set of postulates, for the sake of readability we left out the formal details; see Gärdenfors (1988), Gärdenfors and Makinson (1988).

  9. In fact, this approach opens an interesting perspective on a potential interplay between belief change and argument change, which is however out of the main scope of the present article. A discussion on some recent approaches using belief revision notions inside argumentation is provided in Sect. 8.

  10. For example, see Flouris (2006), Flouris et al. (2008).

  11. For an overview of the literature, see (Fermé and Hansson 2018, Section 5.6).

  12. For an overview of the literature, see (Fermé and Hansson 2018, Section 5.5).

References

  • Alchourrón, C., & Makinson, D. (1981). Hierarchies of regulations and their logic. In Hilpinen, R. (eds.) New studies in deontic logic: Norms, actions, and the foundations of ethics (pp. 125–148).

  • Alchourrón, C., & Makinson, D. (1982). On the logic of theory change: Contraction functions and their associated revision functions. Theoria, 48, 14–37.

    Article  Google Scholar 

  • Alchourrón, C. E., Gärdenfors, P., & Makinson, D. (1985). On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic, 50(2), 510–530.

    Article  Google Scholar 

  • Atkinson, K., Baroni, P., Giacomin, M., Hunter, A., Prakken, H., Reed, C., et al. (2017). Towards artificial argumentation. AI Magazine, 38(3), 25–36.

    Article  Google Scholar 

  • Baroni, P., Caminada, M., & Giacomin, M. (2011). An introduction to argumentation semantics. Knowledge Engineering Review, 26(4), 365–410.

    Article  Google Scholar 

  • Baroni, P., Gabbay, D., Giacomin, M., & van der Torre, L. (eds). (2018). Handbook of formal argumentation. College Publications.

  • Baroni, P., & Giacomin, M. (2007). On principle-based evaluation of extension-based argumentation semantics. Artificial Intelligence, 171(10–15), 675–700.

    Article  Google Scholar 

  • Baroni, P., & Giacomin, M. Semantics of abstract argument systems. In Simari, G. R., & Rahwan, I. (eds.), Argumentation in artificial intelligence (pp. 25–44). Springer.

  • Baroni, P., & Giacomin, M. (2009). Skepticism relations for comparing argumentation semantics. International Journal of Approximate Reasoning, 50(6), 854–866.

    Article  Google Scholar 

  • Baroni, P., Giacomin, M., & Guida, G. (2004). Towards a formalization of skepticism in extension-based argumentation semantics. In Proceedings of 4th workshop on computational models of natural argument (CMNA 2004) (pp. 47–52).

  • Baroni, P., Giacomin, M., & Liao, B. (2018). A general semi-structured formalism for computational argumentation: Definition, properties, and examples of application. Artificial Intelligence, 257, 158–207.

    Article  Google Scholar 

  • Baroni, P., Guida, G., & Giacomin, M. (2007). A- and v-uncertainty: An exploration about uncertainty modeling from a knowledge engineering perspective. International Journal on Artificial Intelligence Tools, 16(2), 161–194.

    Article  Google Scholar 

  • Baroni, P., & Riveret, R. (2019). Enhancing statement evaluation in argumentation via multi-labelling systems. Journal of Artificial Intelligence Research, 66, 793–860.

    Article  Google Scholar 

  • Baumann, R., & Brewka, G. (2015). AGM meets abstract argumentation: Expansion and revision for dung frameworks. In Yang, Q., & Wooldridge, M. J. (eds.), Proceedings of the 24th international joint conference on artificial intelligence, IJCAI 2015, Argentina (pp. 2734–2740). AAAI Press.

  • Baumann, R., & Brewka, G. (2019). Extension removal in abstract argumentation—An axiomatic approach. In The Thirty-Third AAAI conference on artificial intelligence, AAAI 2019, 31st innovative applications of artificial intelligence conference, IAAI 2019, The 9th AAAI Symposium on educational advances in artificial intelligence, EAAI 2019, USA (pp. 2670–2677). AAAI Press.

  • Bench-Capon, T. J. M., & Dunne, P. E. (2007). Argumentation in artificial intelligence. Artificial Intelligence, 171(10–15), 619–641.

    Article  Google Scholar 

  • Besnard, P., García, A. J., Hunter, A., Modgil, S., Prakken, H., Simari, G. R., & Toni, F. (eds.), (2014). Argument & computation—Special issue: Tutorials on structured argumentation5(1). Taylor & Francis Online.

  • Besnard, P., García, A. J., Hunter, A., Modgil, S., Prakken, H., Simari, G. R., & Toni, F. (2014). Introduction to structured argumentation. Argument & Computation, 5(1), 1–4.

    Article  Google Scholar 

  • Besnard, P., & Hunter, A. (2001). A logic-based theory of deductive arguments. Artificial Intelligence, 128(1–2), 203–235.

    Article  Google Scholar 

  • Besnard, P., & Hunter, A. (2008). Elements of argumentation. Cambridge: MIT Press.

    Book  Google Scholar 

  • Birnbaum, L. (1982). Argument molecules: A functional representation of argument structure. In Proceedings of the 2nd national conference on artificial intelligence (AAAI) (pp. 63–65).

  • Birnbaum, L., Flowers, M., & McGuire, R. (1980). Towards an AI model of argumentation. In Proceedings of the 1st national conference on artificial intelligence (AAAI) (pp. 313–315).

  • Bondarenko, A., Dung, P. M., Kowalski, R. A., & Toni, F. (1997). An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence, 93(1–2), 63–101.

    Article  Google Scholar 

  • Booth, R., & Chandler, J. (2016). Extending the Harper identity to iterated belief change. In Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016 (pp. 987–993).

  • Booth, R., & Meyer, T. (2006). Admissible and restrained revision. Journal of Artificial Intelligence Research, 26, 127–151.

    Article  Google Scholar 

  • Boutilier, C. (1993). Revision sequences and nested conditionals. In Proceedings of 13th international joint conference on artificial intelligence (IJCAI’93) (pp. 519–525).

  • Boutilier, C. (1998). A unified model of qualitative belief change: A dynamical systems perspective. Artificial Intelligence, 98(1–2), 281–316.

    Article  Google Scholar 

  • Brewka, G. (2001). Dynamic argument systems: A formal model of argumentation processes based on situation calculus. Journal of Logic and Computation, 11(2), 257–282.

    Article  Google Scholar 

  • Caminada, M. (2018). Rationality postulates: Applying argumentation theory for non-monotonic reasoning. In Baroni, P., Gabbay, D., Giacomin, M., & van der Torre, L. (eds.), Handbook of formal argumentation (pp. 771–795). College Publications.

  • Caminada, M., & Amgoud, L. (2007). On the evaluation of argumentation formalisms. Artificial Intelligence, 171(5–6), 286–310.

    Article  Google Scholar 

  • Carnota, R., & Rodríguez, R. (2011). AGM theory and artificial intelligence. In E. J. Olsson & S. Enqvist (Eds.), Belief revision meets philosophy of science, logic, epistemology, and the unity of science (Vol. 21, pp. 1–42). Berlin: Springer.

    Google Scholar 

  • Casini, G., Fermé, E., Meyer, T., & Varzinczak., I. (2018). A semantic perspective on belief change in a preferential non-monotonic framework. In Proceedings of the sixteenth international conference on principles of knowledge representation and reasoning (KR 2018) (pp. 220–229). AAAI Press.

  • Cayrol, C., Dupin de Saint-Cyr, F., & Lagasquie-Schiex, M.-C. (2010). Change in abstract argumentation frameworks: Adding an argument. Journal of Artificial Intelligence and Reserach (JAIR), 38, 49–84.

    Article  Google Scholar 

  • Chesñevar, C. I., Maguitman, A. G., & Loui, R. P. (2000). Logical models of argument. ACM Computing Surveys, 32(4), 337–383.

    Article  Google Scholar 

  • Chopra, S., & Parikh, R. (1999). An inconsistency tolerant model for belief representation and belief revision. In Proceedings of the sixteenth international joint conference on artificial intelligence (IJCAI 99) (pp. 192–199).

  • Cohen, R. (1981). Investigation of processing strategies for the structural analysis of arguments. In Proceedings of the 19th annual meeting of the association for computational linguistics (pp. 71–75).

  • Coste-Marquis, S., Konieczny, S., Mailly, J.-G., & Marquis, P. (2014a). A translation-based approach for revision of argumentation frameworks. In Fermé, E., & Leite, J. (eds.), Proceedings of the 14th European conference logics in artificial intelligence, JELIA 2014, Portugal, volume 8761 of Lecture Notes in Computer Science (pp. 397–411). Springer.

  • Coste-Marquis, S., Konieczny, S., Mailly, J.-G., & Marquis, P. (2014b). On the revision of argumentation systems: Minimal change of arguments statuses. In Baral, C., De Giacomo, & G., Eiter, T. (eds.), Principles of knowledge representation and reasoning: Proceedings of the 14th international conference, KR 2014, Austria. AAAI Press.

  • Dalal, M. (1988). Investigations into a theory of knowledge base revision: Preliminary report. In Seventh national conference on artificial intelligence, (AAAI-88) (pp. 475–479), St. Paul.

  • Darwiche, A., & Pearl, J. (1997). On the logic of iterated belief revision. Artificial Intelligence, 89(1–2), 1–29.

    Article  Google Scholar 

  • Deagustini, C. A. D., Martinez, M. V., Falappa, M. A., & Simari, G. R. (2019). Belief base contraction by belief accrual. Artificial Intelligence, 275, 78–103.

    Article  Google Scholar 

  • Deagustini, C. A. D., Teze, J. C., Martinez, M. V., Falappa, M. A., & Simari, G. R. (2021). Merging existential rules programs in multi-agent contexts through credibility accrual. Information Sciences, 555, 236–259.

    Article  Google Scholar 

  • Del Val, A. (1997). Non monotonic reasoning and belief revision: Syntactic, semantic, foundational, and coherence approaches. Journal of Applied Non-Classical Logics, 7, 213–240.

    Article  Google Scholar 

  • Delgrande, J. P. (2012). Revising beliefs on the basis of evidence. International Journal of Approximate Reasoning, 53(3), 396–412.

    Article  Google Scholar 

  • Diller, M., Haret, A., Linsbichler, T., Rümmele, S., & Woltran, S. (2018). An extension-based approach to belief revision in abstract argumentation. International Journal of Approximate Reasoning, 93, 395–423.

    Article  Google Scholar 

  • Doutre, S., & Mailly, J.-G. (2018). Constraints and changes: A survey of abstract argumentation dynamics. Argument Computation, 9(3), 223–248.

    Article  Google Scholar 

  • Doyle, J. (1979). A truth maintenance system. Artificial Intelligence, 12, 231–272.

    Article  Google Scholar 

  • Doyle, J. (1992). Reason maintenance and belief revision: Foundations versus coherence theories. In P. Gärdenfors (Ed.), Belief revision (pp. 29–51). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Doyle, J., & London, P. (1980). A selected descriptor-indexed bibliography to the literature on belief revision. SIGART Bulletin, 71, 7–22.

    Article  Google Scholar 

  • Dubois, D., Everaere, P., Konieczny, S., & Papini., O. (2020). Main issues in belief revision, belief merging and information fusion. In A guided tour of artificial intelligence research (pp. 441–485). Springer.

  • Dubois, D., & Prade, H. (1997). Focusing vs. belief revision: A fundamental distinction when dealing with generic knowledge. In Qualitative and quantitative practical reasoning (pp. 96–107). Springer.

  • Dubois, D., Prade, H., & Smets, P. (1996). Representing partial ignorance. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 26(3), 361–377.

    Article  Google Scholar 

  • Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games. Artificial Intelligence, 77(2), 321–357.

    Article  Google Scholar 

  • Dupin de Saint-Cyr, F., Bisquert, P., Cayrol, C., & Lagasquie-Schiex, M.-C. (2016). Argumentation update in YALLA (yet another logic language for argumentation). International Journal of Approximate Reasoning, 75, 57–92.

    Article  Google Scholar 

  • Fagin, R., Ullman, J., & Vardi, M. (1983). On the semantics of updates in databases. In Proceedings of the 2nd ACM SIGACT-SIGMOD symposium on principles of database systems (pp. 352–365).

  • Falappa, M. A., Javier García, A., Kern-Isberner, G., & Simari, G. R. (2013). Stratified belief bases revision with argumentative inference. Journal of Philosophical Logic, 42(1), 161–193.

    Article  Google Scholar 

  • Falappa, M. A., Kern-Isberner, G., & Simari, G. R. (2002). Belief revision, explanations and defeasible reasoning. Artificial Intelligence, 141, 1–28.

    Article  Google Scholar 

  • Fermé, E. (1992). Actualización de bases de conocimiento usando teorías de cambio de creencia. In 3rd Ibero-American conference on artificial intelligence 1992 (pp. 419–436).

  • Fermé, E. (2001). Five faces of recovery. In H. Rott & M.-A. Williams (Eds.), Frontiers in belief revision, applied logic series (pp. 247–259). Boston: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Fermé, E., & Hansson, S. O. (2011). AGM 25 years: Twenty-five years of research in belief change. Journal of Philosophical Logic, 40, 295–331.

    Article  Google Scholar 

  • Fermé, E., & Hansson, S. O. (2018). Belief change: Introduction and overview. Springer Briefs in Computer Science Series. Berlin: Springer.

    Book  Google Scholar 

  • Fermé, E., & Rott, H. (2004). Revision by comparison. Artificial Intelligence, 157, 5–47.

    Article  Google Scholar 

  • Fermé, E., & Wassermann, R. (2018). On the logic of theory change: Iteration of expansion. Journal of the Brazilian Computer Society, 24(1), 8.

    Article  Google Scholar 

  • Fermé, E. L., & Hansson, S. O. (1999). Selective revision. Studia Logica, 63(3), 331–342.

    Article  Google Scholar 

  • Flouris, G. (2006). On belief change in ontology evolution. AI Communications, 19(4), 395–397.

    Google Scholar 

  • Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D., & Antoniou, G. (2008). Ontology change: Classification and survey. The Knowledge Engineering Review, 23(2), 117–152.

    Article  Google Scholar 

  • Friedman, N., & Halpern, J. Y. (1994). A knowledge-based framework for belief change—Part i: Foundations. In Proceedings of Fifth conference on theoretical aspects of reasoning about knowledge (pp. 44–64). Morgan Kaufmann.

  • Fuhrmann, A. (1991). Theory contraction through base contraction. Journal of Philosophical Logic, 20(2), 175–203.

    Article  Google Scholar 

  • García, A. J., & Simari, G. R. (2004). Defeasible logic programming: An argumentative approach. Theory and Practice of Logic Programming, 4(1), 95–138.

    Article  Google Scholar 

  • Gärdenfors, P. (1981). Conditionals and changes of belief. American Philosophical Quarterly, 18(3), 203–211.

    Google Scholar 

  • Gärdenfors, P. (1982). Rules for rational changes of belief. In Pauli, T. (eds.), Philosophical Essays dedicated to Lennart \(\dot{{\rm A}}\)qvist on his fiftieth birthday, number 34 in Philosophical Studies (pp. 88–101).

  • Gärdenfors, P. (1984). Epistemic importance and minimal changes of belief. Australasian Journal of Philosophy, 62, 136–157.

    Article  Google Scholar 

  • Gärdenfors, P. (1986). Belief revisions and the Ramsey test for conditionals. Philosophical Review, 95, 81–93.

    Article  Google Scholar 

  • Gärdenfors, P. (1988). Knowledge in flux: Modeling the dynamics of epistemic states. Cambridge: MIT Press.

    Google Scholar 

  • Gärdenfors, P. (1990). The dynamics of belief systems: Foundations vs. coherence theories. Revue Internationale of Philosophie, 44, 24–46.

    Google Scholar 

  • Gärdenfors, P. (2011). Notes on the history of ideas behind AGM. Journal of Philosophical Logic, 40(2), 115–120.

    Article  Google Scholar 

  • Gärdenfors, P., & Makinson, D. (1988). Revisions of knowledge systems using epistemic entrenchment. In Vardi, M. Y. (eds.), Proceedings of the second conference on theoretical aspects of reasoning about knowledge (pp. 83–95). Los Altos, 1988. Morgan Kaufmann.

  • Gorogiannis, N., & Hunter, A. (2011). Instantiating abstract argumentation with classical logic arguments: Postulates and properties. Artificial Intelligence, 175(9–10), 1479–1497.

    Article  Google Scholar 

  • Hansson, S. O. (1991). Belief contraction without recovery. Studia Logica, 50, 251–260.

    Article  Google Scholar 

  • Hansson, S. O. (1993). Theory contraction and base contraction unified. The Journal of Symbolic Logic, 58(2), 602–625.

    Article  Google Scholar 

  • Hansson, S. O. (1998). Revision of belief sets and belief bases. In Dubois, D., & Prade, H. (eds.), Belief change (pp. 17–75). Springer Netherlands, Dordrecht.

  • Hansson, S. O. (1999a). Recovery and epistemic residue. Journal of Logic, Language and Information, 8(4), 421–428.

  • Hansson, S. O. (1999b). A survey of non-prioritized belief revision. Erkenntnis, 50, 413–427.

  • Hansson, S. O. (2017). Logic of belief revision. In Zalta, E. N. (eds.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, winter 2017.

  • Hansson, S. O., & Olsson, E. (1999). Providing foundations for coherentism. Erkenntnis,51, 243–265.

  • Hansson, S. O., & Wassermann, R. (2002). Local change. Studia Logica, 70(1), 49–76.

    Article  Google Scholar 

  • Hunter, A., & Delgrande, J. P. (2011). Iterated belief change due to actions and observations. Journal of Artificial Intelligence and Research (JAIR), 40, 269–304.

    Article  Google Scholar 

  • Jin, Y., & Thielscher, M. (2007). Iterated belief revision, revised. Artificial Intelligence, 171(1), 1–18.

    Article  Google Scholar 

  • Katsuno, H., & Mendelzon, A. O. (1991). On the difference between updating a knowledge base and revising it. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR’91 (pp. 387–394).

  • Katsuno, H., & Mendelzon, A. O. (1991). Propositional knowledge base revision and minimal change. Artificial Intelligence, 52(3), 263–294.

    Article  Google Scholar 

  • Keller, A. M., & Winslett, M. (1985). On the use of an extended relational model to handle changing incomplete information. IEEE Transactions on Software Engineering, 11(7), 620–633.

    Article  Google Scholar 

  • Konieczny, S., Pino Perez, R. (2017). On iterated contraction: Syntactic characterization, representation theorem and limitations of the Levi identity. In Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds.), Scalable uncertainty management (pp. 348–362), Cham: Springer International Publishing.

  • Koons, R. (2017). Defeasible reasoning. In Zalta, E. N. (eds). The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, winter 2017.

  • Levi, I. (1977). Subjunctives, dispositions, and chances. Synt\(\grave{{\rm h}}\)ese, 34, 423–455.

  • Levi, I. (1991). The fixation of belief and its undoing: Changing beliefs through inquiry. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Liao, B. S., Jin, L., & Koons, R. C. (2011). Dynamics of argumentation systems: A division-based method. Artificial Intelligence, 175(11), 1790–1814.

    Article  Google Scholar 

  • Loui, R. P. (1987). Defeat among arguments: A system of defeasible inference. Computational Intelligence, 3, 100–106.

    Article  Google Scholar 

  • Makinson, D. (1987). On the status of the postulate of recovery in the logic of theory change. Journal of Philosophical Logic, 16, 383–394.

    Article  Google Scholar 

  • Makinson, D. (1996). In memoriam carlos eduardo alchourron. Nordic Journal of Philosophical Logic, 1(1), 3–10.

    Google Scholar 

  • Makinson, D. (1997). Screened revision. Theoria, 63, 14–23.

    Article  Google Scholar 

  • Makinson, D., et al. (1997). On the force of some apparent counterexamples to recovery. pp. In Garzón Valdéz, E. (Ed.), Normative systems in legal and moral theory: Festschrift for Carlos Alchourrón and Eugenio Bulygin (pp. 475–481). Berlin: Duncker & Humblot.

  • Nayak, A. (1994). Foundational belief change. Journal of Philosophical Logic, 23, 495–533.

    Article  Google Scholar 

  • Nebel, B. (1989). A knowledge level analysis of belief revision. In Proceedings of the 1st international conference on principles of knowledge representation and reasoning (KR’89) (pp. 301–311).

  • Nebel, B. (1992). Syntax-based approaches to belief revision. In Gärdenfors, P. (eds.), Belief revision (pp. 52–88). Cambridge University Press.

  • Olsson, E. (2017). Coherentist theories of epistemic justification. In Zalta, E. N. (eds., The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, spring 2017 edition.

  • Peppas, P. (2008). Belief revision. In van Harmelen, F., Lifschitz, V., & Porter, B. (eds.), Handbook of knowledge representation, foundations of artificial intelligence, chapter 8 (pp. 317–359). Elsevier.

  • Peppas, P. (2014). A panorama of iterated revision. In David Makinson on classical methods for non-classical problems (pp. 71–94). Springer.

  • Pollock, J. L. (1987). Defeasible reasoning. Cognitive Science, 11(4), 481–518.

    Article  Google Scholar 

  • Pollock, J. L. (1992). How to reason defeasibly. Artificial Intelligence, 57(1), 1–42.

    Article  Google Scholar 

  • Prakken, H. (2006). Combining sceptical epistemic reasoning with credulous practical reasoning. In Proceedings of of 1st international conference on computational models of argument (COMMA 2006), volume 144 of frontiers in artificial intelligence and applications (pp. 311–322). IOS Press.

  • Prakken, H. (2010). An abstract framework for argumentation with structured arguments. Argument & Computation, 1(2), 93–124.

    Article  Google Scholar 

  • Prakken, H., & Sartor, G. (1997). Argument-based extended logic programming with defeasible priorities. Journal of Applied Non-Classical Logics, 7(1).

  • Prakken, H., & Vreeswijk, G. A. W. (2001). Logics for defeasible argumentation. In Gabbay, D. M., & Guenthner, F. (eds.), Handbook of philosophical logic, second edition. Kluwer Academic Publishers, Dordrecht.

  • Priest, G. (2001). Paraconsistent belief revision. Theoria, 67(3), 214–228.

    Article  Google Scholar 

  • Rahwan, I., & McBurney, P. (2007). Guest editors introduction: Argumentation technology. IEEE Intelligent Systems, 22(6), 21–23.

    Article  Google Scholar 

  • Rahwan, I., & Simari, G. R. (Eds.). (2009). Argumentation in artificial intelligence. Ny: Springer.

  • Rotstein, N. D., Moguillansky, M. O., García, A. J., & Simari, G. R. (2010). A dynamic argumentation framework. In Proceedings of 3rd international conference on computational models of argument (COMMA 2010) (pp. 427–438).

  • Rott, H. (2000). Two dogmas of belief revision. Journal of Philosophy, 97(9), 503–522.

    Article  Google Scholar 

  • Rott, H. (2001). Change, choice and inference. Oxford: Oxford University Press.

    Google Scholar 

  • Rott, H. (2009). Shifting priorities: Simple representations for twenty-seven iterated theory change operators. In Wansing, H., Makinson, D., & Malinowski, J. (eds.), Towards Mathematical Philosophy, number 28 in Trends in Logic (pp. 269–296). Springer Science.

  • Simari, G. R., & Loui, R. P. (1992). A mathematical treatment of defeasible reasoning and its implementation. Artificial Intelligence, 53(2–3), 125–157.

    Article  Google Scholar 

  • Sosa, E. (1980). The raft and the pyramid: Coherence versus foundations in the theory of knowledge. Midwest Studies in Philosophy, 5(1), 3–26.

    Article  Google Scholar 

  • Strasser, C., & Antonelli, G. A. (2019). Non-monotonic Logic. In Zalta, E. N. (eds.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, summer 2019.

  • Testa, R., Fermé, E., Garapa, M., & Reis, M. D. L. (2018). How to construct remainder sets for paraconsistent revisions. In Proceedings of the 13th international workshop on non-monotonic reasoning (NMR) (pp. 119–125), Tempe.

  • Testa, R. R., Coniglio, M. E., & Ribeiro, M. M. (2017). Agm-like paraconsistent belief change. Logic Journal of the IGPL, 25(4), 632–672.

    Article  Google Scholar 

  • van der Torre, L., & Vesic, S. (2018). The principle-based approach to abstract argumentation semantics. In Baroni, P., Gabbay, D., Giacomin, M., & van der Torre, L. (eds.), Handbook of formal argumentation (pp. 797–837). College Publications.

  • van Harmelen, F., Lifschitz, V., & Porter, B. (2008). Handbook of Knowledge Representation: Elsevier Science.

  • Walton, D. (1990). What is reasoning? What is an argument? Journal of Philosophical Logic, 87, 399–419.

    Google Scholar 

  • Walton, D., Reed, C., & Macagno, F. (2008). Argumentation schemes. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Walton, D. N. (1996). Argumentation schemes for presumptive reasoning. Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Walton, D. N., & Krabbe, E. (1995). Commitment in dialogue: Basic concept of interpersonal reasoning. New York: State University of New York Press.

    Google Scholar 

  • Wassermann, R., & Fermé, E. (1999). A note on prototype revision. In Spinning Ideas - Electronic Essays Dedicated to Peter Gärdenfors on His Fiftieth Birthday. LUCS: Und University Cognitive Science, Department of Philosophy, 1999.

  • Winslett, M. (1988). Reasoning about action using a possible models approach. In Proceedings of the seventh American association for artificial intelligence conference (pp. 89–93).

  • Wu, Y., & Caminada, M. W. A. (2010). A labelling-based justification status of arguments. Studies in Logic, 3(4), 12–29.

    Google Scholar 

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Acknowledgements

We are grateful to the anonymous reviewers for their constructive comments that helped us improve the original manuscript. E.F. was partially supported by FCT through project UID/CEC/04516/2019 (NOVA LINCS) and projects PTDC/CCI-COM/30990/2017 and PTDC/CCI-COM/4464/2020. G.R.S. has been partially supported by EU H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 690974 for the project MIREL: MIning and Reasoning with Legal texts, by funds provided by Dep. de Ciencias e Ingeniería de la Computación (DCIC), Universidad Nacional del Sur, & Instituto de Ciencias e Ingeniería de la Computación (ICIC UNS-CONICET), Argentina.

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Correspondence to Guillermo Ricardo Simari.

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Baroni, P., Fermé, E., Giacomin, M. et al. Belief Revision and Computational Argumentation: A Critical Comparison. J of Log Lang and Inf 31, 555–589 (2022). https://doi.org/10.1007/s10849-022-09369-8

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