Shape predicates allow unbounded verification of linearizability using canonical abstraction

Canonical abstraction is a static analysis technique that represents states as 3-valued logical structures, and is able to construct finite representations of systems with infinite statespaces for verification. The granularity of the abstraction can be altered by the definition of instrumentation predicates, which derive their meaning from other predicates. We introduce shape predicates for preserving certain structures of the state during abstraction. We show that shape predicates allow linearizability to be verified for concurrent data structures using canonical abstraction alone, and use the approach to verify a stack and two queue algorithms. This contrasts with previous efforts to verify linearizability with canonical abstraction, which have had to employ other techniques as well

We are Designers Because We Can Abstract

Organised by: Cranfield UniversityDue to the increasing systems complexity, architecture design became an important issue. It gained interest and its importance was framed in three domains: as a way to understand complex systems, to design them, to manage their manufacturing process and to provide long-term rationality. The purpose of this paper is, firstly, to survey the existing definition approaches on architecture. Secondly, we propose a model for architecture design which articulates the potential linkage between two principle concepts: synthesis and abstraction. Our proposal model focuses on abstraction concept and permits an effective top-down design approach. It helps also designers to more respond to issues that characterize architecture design.Mori Seiki – The Machine Tool Compan

Causal Abstraction with Soft Interventions

Causal abstraction provides a theory describing how several causal models can represent the same system at different levels of detail. Existing theoretical proposals limit the analysis of abstract models to "hard" interventions fixing causal variables to be constant values. In this work, we extend causal abstraction to "soft" interventions, which assign possibly non-constant functions to variables without adding new causal connections. Specifically, (i) we generalize $\tau$-abstraction from Beckers and Halpern (2019) to soft interventions, (ii) we propose a further definition of soft abstraction to ensure a unique map $\omega$ between soft interventions, and (iii) we prove that our constructive definition of soft abstraction guarantees the intervention map $\omega$ has a specific and necessary explicit form

Optimal infinite scheduling for multi-priced timed automata

This paper is concerned with the derivation of infinite schedules for timed automata that are in some sense optimal. To cover a wide class of optimality criteria we start out by introducing an extension of the (priced) timed automata model that includes both costs and rewards as separate modelling features. A precise definition is then given of what constitutes optimal infinite behaviours for this class of models. We subsequently show that the derivation of optimal non-terminating schedules for such double-priced timed automata is computable. This is done by a reduction of the problem to the determination of optimal mean-cycles in finite graphs with weighted edges. This reduction is obtained by introducing the so-called corner-point abstraction, a powerful abstraction technique of which we show that it preserves optimal schedules

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

Abductive and Consistency-Based Diagnosis Revisited: a Modeling Perspective

Diagnostic reasoning has been characterized logically as consistency-based reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content of the knowledge base [Console&Torasso91], and, on the other hand, that, depending on the choice of the definition the same knowledge should be expressed in different form [Poole94]. Since in Model-Based Diagnosis a major problem is finding the right way of abstracting the behavior of the system to be modeled, this paper discusses the relation between modeling, and in particular abstraction in the model, and the notion of diagnosis.Comment: 5 pages, 8th Int. Workshop on Nonmonotonic Reasoning, 200

Towards an ontology-based platform-independent framework for developing KBE systems in the aerospace industry

Aerospace engineering is considered to be one of the most complex and advanced branches of engineering. The use of knowledge based engineering (KBE) technologies has played a major role in automating routine design activities in view of supporting the cost-effective and timely development of a product. However, technologies employed within KBE systems are usually platform-specific. The nature of these platform-specific models has significantly limited knowledge abstraction and reusability in KBE systems. This research paper presents a novel approach that illustrates the use of platform-independent knowledge models for the development of KBE systems in the aerospace industry. The use of semantic technologies through the definition of generic-purposed ontologies has been employed to support the notion of independent knowledge models that strengthens knowledge reusability in KBE systems. This approach has been validated qualitatively through experts’ opinion and its benefit realised in the abstraction, reusability and maintainability of KBE systems