Uncertainty often pervades information and knowledge. For this reason, the handling of
uncertainty in inference systems has been an issue for a long time in artificial intelligence …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Many real world domains require the representation of a measure of uncertainty. The most
common such representation is probability, and the combination of probability with logic …

Multi-adjoint logic programming is a general framework with interesting features, which
involves other positive logic programming frameworks such as monotonic and residuated …

This brief overview provides a quick survey of qualitative possibility theory and possibilistic
logic along with their applications to various forms of epistemic reasoning under and about …

The use of knowledge-based systems has been shown to be a suitable approach to support
decision making in environmental systems. Capturing and managing the huge quantity of …

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by
attaching to each rule a degree of certainty. While such an extension is important from an …

In this paper, we propose a multi-agent framework to deal with situations involving uncertain
or inconsistent information located in a distributed environment which cannot be combined …

Learning from 1-step transitions (LF1T) has become a paradigm to construct a logical
hypothesis of a dynamic system, such as a Boolean network, from its synchronized state …

Answer set programming is a form of declarative programming that can be used to elegantly
model various systems. When the available knowledge about these systems is imperfect …