Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying many AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a lattice-valued possibilistic logic, and a Dempster-Shafer representation
In this work, we introduce a new framework able to deal with a reasoning that is at the same time no...
This paper defines a logic model of optimization under uncertainty which optimizes the expectation o...
In this paper, we will develop a class of logics for reasoning about qualitative and quantitative un...
AbstractLinear constraints occur naturally in many reasoning problems and the information that they ...
Abstract. Linear constraints occur naturally in many reasoning problems and the information that the...
Several logics for reasoning under uncertainty distribute probabil ity mass over sets in some sense ...
Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfactio...
We consider linear programming problems with uncertain constraint coefficients described by interval...
Abstract Constraint problems with incomplete or erroneous data are often sim-plified to tractable de...
AbstractWe describe the basic ideas of the theory of approximate reasoning and indicate how it provi...
AbstractDescription logics (DLs) play an important role in the Semantic Web as the foundation of ont...
We treat in this paper linear programming (LP) problems with uncertain data. The focus is on uncerta...
We treat in this paper Linear Programming (LP) problems with uncertain data. The focus is on uncerta...
Constraint handling rules are a committed-choice language consisting of multiple-heads guarded rules...
International audienceLinear implication can represent state transitions, but real transition system...
In this work, we introduce a new framework able to deal with a reasoning that is at the same time no...
This paper defines a logic model of optimization under uncertainty which optimizes the expectation o...
In this paper, we will develop a class of logics for reasoning about qualitative and quantitative un...
AbstractLinear constraints occur naturally in many reasoning problems and the information that they ...
Abstract. Linear constraints occur naturally in many reasoning problems and the information that the...
Several logics for reasoning under uncertainty distribute probabil ity mass over sets in some sense ...
Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfactio...
We consider linear programming problems with uncertain constraint coefficients described by interval...
Abstract Constraint problems with incomplete or erroneous data are often sim-plified to tractable de...
AbstractWe describe the basic ideas of the theory of approximate reasoning and indicate how it provi...
AbstractDescription logics (DLs) play an important role in the Semantic Web as the foundation of ont...
We treat in this paper linear programming (LP) problems with uncertain data. The focus is on uncerta...
We treat in this paper Linear Programming (LP) problems with uncertain data. The focus is on uncerta...
Constraint handling rules are a committed-choice language consisting of multiple-heads guarded rules...
International audienceLinear implication can represent state transitions, but real transition system...
In this work, we introduce a new framework able to deal with a reasoning that is at the same time no...
This paper defines a logic model of optimization under uncertainty which optimizes the expectation o...
In this paper, we will develop a class of logics for reasoning about qualitative and quantitative un...