The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Non-standard theories of uncertainty of plausible reasoning
Principles of knowledge representation
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
Belief functions and default reasoning
Artificial Intelligence
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Learning Conditional Information in Possibility Theory
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Maximum entropy and variable strength defaults
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Postulates for conditional belief revision
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
The Principle of Conditional Preservation in Belief Revision
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
Representing and Learning Conditional Information in Possibility Theory
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Qualitative Knowledge Discovery
Semantics in Data and Knowledge Bases
| Hi-index | 0.00 |
Conditionals ("if-then-rules") are most important objects in knowledge representation, commonsense reasoning and belief revision. Due to their non-classical nature, however, they are not easily dealt with. This paper presents a new approach to conditionals, which is apt to capture their dynamic power peculiarly well. We show how this approach can be applied to represent conditional knowledge inductively. In particular, we generalize system-Z* as an appropriate counterpart to maximum entropy-representations in a semi-quantitative setting.