A Possibilistic Argumentation Decision Making Framework with Default Reasoning

• Autorzy: Nieves J.C. , Confalonieri R.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we introduce a possibilistic argumentation-based decision making framework which is able to capture uncertain information and exceptions/defaults. In particular, we define the concept of a possibilistic decision making framework which is based on a possibilistic default theory, a set of decisions and a set of prioritized goals. This set of goals captures user preferences related to the achievement of a particular state in a decision making problem. By considering the inference of the possibilistic well-founded semantics, the concept of argument with respect to a decision is defined. This argument captures the feasibility of reaching a goal by applying a decision in a given context. The inference in the argumentation decision making framework is based on basic argumentation semantics. Since some basic argumentation semantics can infer more than one possible scenario of a possibilistic decision making problem, we define some criteria for selecting potential solutions of the problem.

Słowa kluczowe

EN

argumentation theory; well-founded semantics; decision making problem

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2011
• Tom: Vol. 113, nr 1
• Strony: 41--61
• Opis fizyczny: Bibliogr. 38 poz.

Twórcy

autor

Nieves J.C.

autor

Confalonieri R.

• Universitat Politecnica de Catalunya, Dept. Llenguatges i Sistemes Informatics, Edifici K2M, Despatx 201, C/ Jordi Girona Salgado 1-3, E - 08034 Barcelona, Spain,

Bibliografia

• [1] Alsinet, T., Chesñevar, C. I., Godo, L., Sadri, S., Simari, G. R.: Formalizing Argumentative Reasoning in a Possibilistic Logic Programming Setting with Fuzzy Unification, International Journal of Approximate Reasoning, 48(3), August 2008, 711-729.
• [2] Alsinet, T., Chesñevar, C. I., Godo, L., Simari, G. R.: A logic programming framework for possibilistic argumentation: Formalization and logical properties, Fuzzy Sets and Systems, 159(10), 2008, 1208-1228.
• [3] Alsinet, T., Godo, L.: A Complete Calculus for Possibilistic Logic Programming with Fuzzy Propositional Variables, Proceedings of the Sixteen Conference on Uncertainty in Artificial Intelligence, ACMPress, 1-10, 2000.
• [4] Amgoud, L., Cayrol, C.: Inferring from Inconsistency in Preference-Based Argumentation Frameworks, Journal of Automated Reasoning, 29(2), 2002, 125-169.
• [5] Amgoud, L., Prade, H.: Using arguments for making and explaining decisions, Artificial Intelligence, 173(3-4), March 2009, 413-436.
• [6] Aulinas, M., Nieves, J. C., Poch, M., Cortés, U.: Supporting Decision Making in UrbanWastewater Systems using a Knowledge-Based Approach, Environmental Modelling and Software, 26(5), 2011, 562-572.
• [7] Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving, Cambridge University Press, Cambridge, 2003.
• [8] Baroni, P., Giacomin, M.: On principle-based evaluation of extension-based argumentation semantics, Artificial Intelligence., 171(10-15), 2007, 675-700.
• [9] Baroni, P., Giacomin, M., Guida, G.: SCC-recursiveness: a general schema for argumentation semantics, Artificial Intelligence, 168(1-2), October 2005, 162-210.
• [10] Bench-Capon, T. J. M., Dunne, P. E.: Argumentation in artificial intelligence, Artificial Intelligence, 171(10-15), 2007, 619-641.
• [11] Bonet, B., Geffner, H.: Arguing for Decisions: A Qualitative Model of Decision Making, Proceedings of the 12th Annual Conference on Uncertainty in Artificial Intelligence (UAI-96), Morgan Kaufmann Publishers, San Francisco, CA, 1996.
• [12] Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline Operator, Proceedings of the 17th International Conference on Data Engineering, IEEE Computer Society, Washington, DC, USA, 2001, ISBN 0-7695-1001-9.
• [13] Brass, S., Zukowski, U., Freitag, B.: Transformation-Based Bottom-Up Computation of the Well-Founded Model., NMELP, 1996.
• [14] Brewka, G.: Answer Sets and Qualitative DecisionMaking, Synthese, 146, 2005, 171-187, ISSN 0169-2968.
• [15] Carofiglio, V.: Modelling Argumentation with Belief Networks, CMNA IV, 4th Workshop on Computational Models of Natural Argument (F. Grasso, C. Reed, G. Carenini, Eds.), ECAI 2004, Valencia, Spain, 2004.
• [16] Dix, J.: A Classification Theory of Semantics of Normal Logic Programs: I. Strong Properties., Fundamenta Informaticae, 22(3), 1995, 227-255.
• [17] Dix, J.: A Classification Theory of Semantics of Normal Logic Programs: II.Weak Properties., Fundamenta Informaticae, 22(3), 1995, 257-288.
• [18] Dix, J., Osorio, M., Zepeda, C.: A general theory of confluent rewriting systems for logic programming and its applications., Annals of Pure and Applied Logic, 108(1-3), 2001, 153-188.
• [19] Dubois, D., Lang, J., Prade, H.: Possibilistic Logic, in: Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning (D. Gabbay, C. J. Hogger, J. A. Robinson, Eds.), Oxford University Press, Oxford, 1994, 439-513.
• [20] Dubois, D., Prade, H.: Possibility theory as a basis for qualitative decision theory, Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1995, ISBN 1-55860-363-8.
• [21] Dung, P. M.: On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games., Artificial Intelligence, 77(2), 1995, 321-358.
• [22] Dung, P.M., Thang, P.M., Toni, F.: Towards argumentation-based contract negotiation, Second International Conference on Computational Models of Argument (COMMA'08), 172, IOS Press, 2008.
• [23] Fox, J., Glasspool, D., Grecu, D., Modgil, S., South, M., Patkar, V.: Argumentation-Based Inference and Decision Making - A Medical Perspective, IEEE Intelligence Systems, 22(6), November/December 2007, 34-41.
• [24] Fox, J., Parsons, S.: Applications of Uncertainty Formalisms, chapter Arguing about beliefs and actions, Springer-Verlag, Berlin, 1998, 266-302.
• [25] Garcia, D., Nieves, J. C., Cortés, U.: Reasoning about Actions for the Management of Urban Wastewater Systems using a Causal Logic, International Congress on Environmental Modelling and Software, Ottawa, Canada, 2010.
• [26] Gelder, A. V., Ross, K. A., Schlipf, J. S.: TheWell-Founded Semantics for General Logic Programs., Journal of the ACM, 38(3), 1991, 620-650.
• [27] Gelfond, M., Lifschitz, V.: The Stable Model Semantics for Logic Programming, 5th Conference on Logic Programming (R. Kowalski, K. Bowen, Eds.), MIT Press, 1988.
• [28] Halpern, J. Y.: Reasoning about uncertainty, The MIT Press, 2005.
• [29] Krause, P., Ambler, S., Elvang-Gøransson, M., Fox, J.: A Logic of Argumentation for Reasoning under Uncertainty., Computational Intelligence, 11, 1995, 113-131.
• [30] Nicolas, P., Garcia, L., Stéphan, I., Lef`evre, C.: Possibilistic Uncertainty Handling for Answer Set Programming, Annals of Mathematics and Artificial Intelligence, 47(1-2), June 2006, 139-181.
• [31] Nieves, J. C., Osorio, M., Cortés, U.: Semantics for Possibilsitic Disjuntive Programs, Theory and Practice of Logic Programming, 2011, DOI: 10.1017/S1471068411000408.
• [32] Nieves, J. C., Osorio, M., Zepeda, C.: A Schema for Generating Relevant Logic Programming Semantics and its Applications in Argumentation Theory, Fundamenta Informaticae, 106(2-4), 2011, 295-319.
• [33] Osorio, M., Nieves, J. C.: Possibilistic Well-Founded Semantics, 8th Mexican International Conference on Artificial Intelligence, MICAI'09, 5845, Springer-Verlag, 2009.
• [34] Pelletier, F. J., Elio, R.: Scope of Logic, Methodology and Philosophy of Science, vol. 1 of Synthese Library, chapter Logic and Computation, Dordrecht: Kluwer Academic Press, 2002, 137-156.
• [35] Prakken, H., Vreeswijk, G. A. W.: Logics for defeasible argumentation, in: Handbook of Philosophical Logic (D. Gabbay, F. Günthner, Eds.), vol. 4, second edition, Kluwer Academic Publishers, Dordrecht/Boston/London, 2002, 219-318.
• [36] Savage, L. J.: The Foundations of Statistics, Dover Publications, Inc., New York, 1972.
• [37] Toni, F.: Argumentative Agents, International Multiconference on Computer Science and Information Technology, 5, 2010.
• [38] Wooldridge, M.: Reasoning about Rational Agents, MIT Press, Cambridge, USA, 2000.