Semantical considerations on nonmonotonic logic
Artificial Intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
On the relation between default and autoepistemic logic
Artificial Intelligence
Making data structures persistent
Journal of Computer and System Sciences - 18th Annual ACM Symposium on Theory of Computing (STOC), May 28-30, 1986
Probabilistic semantics for nonmonotonic reasoning: a survey
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
ACM Computing Surveys (CSUR)
A tutorial on default reasoning
The Knowledge Engineering Review
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Sequential thresholds: context sensitive default extensions
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
When we work with information from multiple sources, the formalism each employs to handle uncertainty may not be uniform. In order to be able to combine these knowledge bases of different formats, we need to first establish a common basis for characterizing and evaluating the different formalisms, and provide a semantics for the combined mechanism. A common framework can provide an infrastructure for building an integrated system, and is essential if we are to understand its behavior. We present a unifying framework based on an ordered partition of possible worlds called partition sequences, which corresponds to our intuitive notion of biasing towards certain possible scenarios when we are uncertain of the actual situation. We show that some of the existing formalisms, namely, default logic, autoepistemic logic, probabilistic conditioning and thresholding (generalized conditioning), and possibility theory can be incorporated into this general framework.