The complexity of the real world and the restricted availability of knowledge call for powerful logical frameworks to deal with uncertainty, reaching from qualitative default formalisms to quantitative probability logics. In particular, reasoning under uncertainty requires defeasible inference notions. We take a general look at these issues, with a special emphasis on quasi-probabilistic approaches to default reasoning
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
AbstractDefault rules express concise pieces of knowledge having implicit exceptions, which is appro...
We propose a general scheme for adding probabilistic reasoning capabilities to any knowledge represe...
The complexity of the real world and the restricted availability of knowledge call for powerful logi...
Default rules express concise pieces of knowledge having implicit exceptions, which is appropriate f...
International audienceDefault rules express concise pieces of knowledge having implicit exceptions, ...
A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'L...
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is...
In this paper we present a novel approach to default reasoning, based on the concept of A-uncertaint...
We describe a new approach to default reasoning, based on a principle of indifference among possible...
In this paper, we present two results on combining approximate and default reasoning. First, we inve...
In this paper, we develop a model for uncertain default reasoning. It has the following characterist...
Abstract. Many frameworks have been proposed to manage uncertain informa-tion in logic programming. ...
Several attempts to define formal logic5 for some type of default reasoning have been made. All of t...
A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. Li...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
AbstractDefault rules express concise pieces of knowledge having implicit exceptions, which is appro...
We propose a general scheme for adding probabilistic reasoning capabilities to any knowledge represe...
The complexity of the real world and the restricted availability of knowledge call for powerful logi...
Default rules express concise pieces of knowledge having implicit exceptions, which is appropriate f...
International audienceDefault rules express concise pieces of knowledge having implicit exceptions, ...
A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'L...
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is...
In this paper we present a novel approach to default reasoning, based on the concept of A-uncertaint...
We describe a new approach to default reasoning, based on a principle of indifference among possible...
In this paper, we present two results on combining approximate and default reasoning. First, we inve...
In this paper, we develop a model for uncertain default reasoning. It has the following characterist...
Abstract. Many frameworks have been proposed to manage uncertain informa-tion in logic programming. ...
Several attempts to define formal logic5 for some type of default reasoning have been made. All of t...
A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. Li...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
AbstractDefault rules express concise pieces of knowledge having implicit exceptions, which is appro...
We propose a general scheme for adding probabilistic reasoning capabilities to any knowledge represe...