Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic semantics for nonmonotonic reasoning: a survey
Readings in uncertain reasoning
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Institutions: abstract model theory for specification and programming
Journal of the ACM (JACM)
The emergence of ordered belief from initial ignorance
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
Using Institutions for the Study of Qualitative and Quantitative Conditional Logics
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Reasoning about Uncertainty
The relationship of the logic of big-stepped probabilities to standard probabilistic logics
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Semantical investigations into nonmonotonic and probabilistic logics
Annals of Mathematics and Artificial Intelligence
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Various semantics have been used for conditionals in the area of knowledge representation and reasoning. In this paper, we study similarities and differences between a purely qualitative semantics based on the popular system-of-spheres semantics of Lewis, an ordinal semantics making use of rankings, a possibilistic semantics, and a semantics representing conditionals by probabilities in a qualitative way. As a common framework for the corresponding logics, we use Goguen and Burstall's notion of institutions whose central motto is that truth is invariant under the change of notation. The institution framework provides the formal rigidity needed for our investigation, but leaves enough abstract freedom to formalize and compare quite different logics. We show precisely in which sense the conditional semantics mentioned above are logically similar, and point out the semantical subtleties each semantics allows.