Interventions in possibilistic logic

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5785 LNAI, Issue , 2009, Pages 40-54

  Benferhat, Salem ^a   Dubois, Didier ^b   Prade, Henri ^b  

^a UNIVERSITÉ D'ARTOIS   (France)

^b UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; CAUSAL RELATIONS; NET STRUCTURES; POSSIBILISTIC; POSSIBILISTIC LOGIC;

KNOWLEDGE REPRESENTATION;

EID: 70350436463     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04388-8_5     Document Type: Conference Paper

References (15)

1. Benferhat, S., Dubois, D., Garcia, L., Prade, H.: On the transformation between possibilistic logic bases and possibilistic causal networks. Inter. J. of Approximate Reasoning 29, 135-173 (2002)
2. Benferhat, S., Dubois, D., Prade, H.: Nonmonotonic reasoning, conditional objects and possibility theory. Artificial Intelligence 92, 259-276 (1997)
3. Benferhat, S., Smaoui, S.: Possibilistic causal networks for handling interventions: A new propagation algorithm. In: Proc. 22nd AAAI Conf. on Artif. Intelligence (AAAI 2007), July 2007, pp. 373-378 (2007)
4. Bonnefon, J.F., Da Silva Neves, R.M., Dubois, D., Prade, H.: Background default knowledge and causality ascriptions. In: Proc. 17th Europ. Conf. on Artif. Intel (ECAI 2006), vol. 29(8-1(9)), pp. 11-15. Riva del Garda (2006)
5. Dubois, D., Lang, J., Prade, H.: Possibilistic logic. In: Gabbay, D., Hogger, C., Robin-son, J., Nute, D. (eds.) Handbook de Logic in Artificial Intelligence and Logic Programming, vol. 3, pp. 439-513. Oxford Univ. Pr., Oxford (1994)
6. Dubois, D., Prade, H.: Modeling the role of (ab)normality in the ascription of causality judgements by agents. In: Morgenstern, L., Pagnucco, M. (eds.) Proc. of IJCAI-05 Workshop on Nonmonotonic Reasoning, Action, and Change (NRAC 2005), Edinburg, August 1, pp. 22-27 (2005)
7. Goldszmidt, M., Pearl, J.: Rank-based Systems: A Simple Approach to Belief Revision, belief update, and reasoning about evidence and actions. In: Proc. of 3rd Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR 1992), Cambridge, MA, October 25-29, pp. 661-672. Morgan Kaufmann, San Francisco (1992)
8. Halpern, J., Pearl, J.: Causes and explanations: a structural-model approach. Part I: Causes. Part II: Explanations. British Journal for the Philosophy of Science 56, 843-887, 889-911 (2005)
9. Kraus, S., Lehmann, D., Magidor, M.: Nonmonotonic reasoning, preferential models and cumulative logics. Arti cial Intelligence 44, 167-207 (1990)
10. Pearl, J.: Embracing causality in default reasoning. Artificial Intelligence 35, 259-271 (1988)
11. Pearl, J.: A probabilistic calculus of actions. In: Lopez de Mantaras, R., Poole, D. (eds.) Proc. 10th Conf. Uncertainty in A I (UAI-1994), Seattle, July 29-31, pp. 454-462. Morgan Kaufmann, San Francisco (1994)
12. Pearl, J.: Causality. Cambridge Univ. Pr., Cambridge (2000)
13. Sloman, S.A., Lagnado, D.A.: Do we "do"? Cognitive Science 29, 5-39 (2005)
14. Spohn, W.: A general non-probabilistic theory of inductive reasoning. In: Shachter, R., Levitt, T., Kanal, L., Lemmer, J. (eds.) Uncertainty in Artificial Intelligence, North-Holland, vol. 4, pp. 149-158 (1990)
15. Woodward, J.: Causation and manipulability. In: Zalta, E.N. (ed.) Stanford Encyclopedia of Philosophy (2001) http://plato.stanford.edu/entries/ causation-mani/