Completeness is an important, but rather uncommon, property of abstract interpretations, ensuring that abstract computations are as precise as possible w.r.t. concrete ones. It turns out that completeness for an abstract interpretation depends only on its underlying abstract domains, and therefore it is an abstract domain property. Recently, the first two authors proved that for a given abstrac...

interpretation and infinite state model checking In abstract interpretation we develop an abstract semantic function systematically from the standard (“concrete”) semantics. The formal framework is based on a Galois connection 〈L,vL〉 −−−→ ←−−− α γ 〈M,vM 〉 between the lattices 〈L,vL〉 and 〈M,vM 〉 which are the concrete and abstract semantic domains respectively. The functions α and γ are known as...

We present an explanation-oriented, domain-specific, visual language for explaining probabilistic reasoning. Explanation-oriented programming is a new paradigm that shifts the focus of programming from the computation of results to explanations of how those results were computed. Programs in this language therefore describe explanations of probabilistic reasoning problems. The language relies o...

background: protein-protein interactions do not provide any direct information re­garding the domains within the proteins that mediate the interactions. the majority of proteins are multi domain proteins and the interaction between them is often defined by the pairs of their domains. most of the former studies focus only on interacting do­main pairs. however they do not consider the interaction...

Interpretation Consider C = ℘(Z): [Cousot & Cousot’77] C A {0} {0,!1,!2,!3,...} 0! {!2,!3} Abstract domain 0+ ? {0,1,2,3,...} Language-based Security: Abstract Non-Interferece – p.3/32 Abstract Interpretation Consider C = ℘(Z): [Cousot & Cousot’77]Interpretation Consider C = ℘(Z): [Cousot & Cousot’77] Abstract domain C A {0} {0,1,2,3,...} {0,!1,!2,!3,...} 0! {!2,!3} 0+ ?domain C A {0} {0,1,2,3,...

Learning the right graph representation from noisy, multi-source data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Here the objective is to incorporate the partial information each data source gives us in a way that captures the true underlying relationships. To address this challenge, we present a general, boosting-inspired framewor...

An explicit representation of the problem solving method of an expert system shell as abstract control knowledge provides a powerful foundation for learning. This paper describes the abstract control knowledge of the HERACLES expert system shell for heuristic classification problems, and describes how the ODYSSEUS apprenticeship learning program uses this representation to semi-automate “end-ga...

The Tools and Materials Metaphor is a design methodology for interactive software systems. It supports developers with both metaphors on how to interpret and understand an application domain as well as concrete techniques on how to actually construct systems according to the metaphors. The construction process is best supported by an application framework that captures those parts of the metaph...

We define the inverse operation for disjunctive completion, introducing the notion of least disjunctive basis for an abstract domain D: this is the most abstract domain inducing the same disjunctive completion as D. We show that the least disjunctive basis exists in most cases, and study its properties in relation with reduced product of abstract interpretations. The resulting framework is powe...

We study orderings £S on reductions in the style of Lévy reflecting the growth of information w.r.t. (super)stable sets S of ‘values’ (such as head-normal forms or Böhm-trees). We show that sets of co-initial reductions ordered by £S form finitary ω-algebraic complete lattices, and hence form computation and Scott domains. As a consequence, we obtain a relativized version of the computational s...