Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
A logical framework for default reasoning
Artificial Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Conditional entailment: bridging two approaches to default reasoning
Artificial Intelligence
A logic for reasoning with inconsistent knowledge
Artificial Intelligence
Reasoning about priorities in default logic
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Handling Partially Ordered Defaults in TMS
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Well-founded semantics for extended logic programs with dynamic preferences
Journal of Artificial Intelligence Research
Preference-based search and multi-criteria optimization
Eighteenth national conference on Artificial intelligence
Preference programming: Advanced problem solving for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Journal of Artificial Intelligence Research
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Explicit preferences on assumptions as used in prioritized circumscription [McCarthy, 1986; Lifschitz, 1985; Grosof, 1991] and preferred subtheories [Brewka, 1989] provide a clear and declarative method for defining preferred models. In this paper, we show how to embed preferences in the logical theory itself. This gives a high freedom for expressing statements about preferences. Preferences can now depend on other assumptions and are thus dynamic. We elaborate a preferential semantics based on Lehmann's cumulative models, as well as a corresponding constructive characterization, which specifies how to correctly treat dynamic preferences in the default reasoning system EXCEPT. [Junker, 1992].