Integrating Preference Orderings into Argument-Based Reasoning
作者： Leila Amgoud , Claudette Cayrol
DOI: 10.1007/BFB0035620
关键词:
摘要: Argument-based reasoning is a promising approach to handle inconsistent belief bases. The basic idea justify each plausible conclusion by acceptable arguments. purpose of this paper enforce the concept acceptability integration preference orderings. Pursuing previous work on principles preference-based argumentation, we focus here definition new classes
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sci-hub.se 参考文章(15)
Salem Benferhat, Henri Prade, Didier Dubois, How to infer from inconsistent beliefs without revising international joint conference on artificial intelligence. pp. 1449- 1455 ,(1995)
Claudette Cayrol, On the relation between argumentation and non-monotonic coherence based entailment international joint conference on artificial intelligence. pp. 1443- 1448 ,(1995)
C. Cayrol, V. Royer, C. Saurel, Management of Preferences in Assumption-Based Reasoning international conference information processing. pp. 13- 22 ,(1992) , 10.1007/3-540-56735-6_39
Gerard Vreeswijk, The Feasibility of Defeat in Defeasible Reasoning principles of knowledge representation and reasoning. pp. 359- 380 ,(1993) , 10.1007/978-94-015-8242-1_13
Morten Elvang-Gøransson, Paul J. Krause, John Fox, Acceptability of arguments as `logical uncertainty' european conference on symbolic and quantitative approaches to reasoning and uncertainty. pp. 85- 90 ,(1993) , 10.1007/BFB0028186
Henry Prakken, Giovanni Sartor, A System for Defeasible Argumentation, with Defeasible Priorities Artificial Intelligence Today. pp. 365- 379 ,(1999) , 10.1007/3-540-48317-9_15
Claudette Cayrol, From Non-Monotonic Syntax-Based Entailment to Preference-Based Argumentation european conference on symbolic and quantitative approaches to reasoning and uncertainty. pp. 99- 106 ,(1995) , 10.1007/3-540-60112-0_12
Claudette Cayrol, Marie-Christine Lagasquie-Schiex, Non-monotonic Syntax-Based Entailment: A Classification of Consequence Relations european conference on symbolic and quantitative approaches to reasoning and uncertainty. pp. 107- 114 ,(1995) , 10.1007/3-540-60112-0_13
Anthony Hunter, Defeasible Reasoning with Structured Information. principles of knowledge representation and reasoning. pp. 281- 292 ,(1994) , 10.1016/B978-1-4832-1452-8.50122-6
John L. Pollock, How to reason defeasibly Artificial Intelligence. ,vol. 57, pp. 1- 42 ,(1992) , 10.1016/0004-3702(92)90103-5