This paper presents Tweety, an open source project for scientific experimentation on logical
aspects of artificial intelligence and particularly knowledge representation. Tweety provides …

Abstract Probabilistic Logic Programs under the distribution semantics (PLPDS) do not allow
statistical probabilistic statements of the form “90% of birds fly”, which were defined “Type 1” …

Inconsistencies in knowledge bases are of major concern in knowledge representation and
reasoning. In formalisms that employ model-based reasoning mechanisms inconsistencies …

Tweety is a collection of Java libraries that provides a general interface layer for doing
research in and working with different knowledge representation formalisms such as …

In 1990, Halpern proposed the distinction between Type 1 and Type 2 statements: the
former express statistical information about a domain of interest while the latter define a …

A generative probabilistic model for relational data consists of a family of probability
distributions for relational structures over domains of different sizes. In most existing …

Abstract “Type 1” statements were introduced by Halpern in 1990 with the goal to represent
statistical information about a domain of interest. These are of the form “x% of the elements …

Evidence from studies, such as in science or medicine, often corresponds to conditional
probability statements. Furthermore, evidence can conflict, in particular when coming from …

We consider the problem of reasoning under uncertainty in the presence of inconsistencies.
Our knowledge bases consist of linear probabilistic constraints that, in particular, generalize …

The principle of maximum entropy has proven to be a powerful approach for commonsense
reasoning in probabilistic conditional logics on propositional languages. Due to this …