35,670 research outputs found
The natural history of bugs: using formal methods to analyse software related failures in space missions
Space missions force engineers to make complex trade-offs between many different constraints including cost, mass, power, functionality and reliability. These constraints create a continual need to innovate. Many advances rely upon software, for instance to control and monitor the next generation ‘electron cyclotron resonance’ ion-drives for deep space missions.Programmers face numerous challenges. It is extremely difficult to conduct valid ground-based tests for the code used in space missions. Abstract models and simulations of satellites can be misleading. These issues are compounded by the use of ‘band-aid’ software to fix design mistakes and compromises in other aspects of space systems engineering. Programmers must often re-code missions in flight. This introduces considerable risks. It should, therefore, not be a surprise that so many space missions fail to achieve their objectives. The costs of failure are considerable. Small launch vehicles, such as the U.S. Pegasus system, cost around 4 million up to 73 million from the failure of a single uninsured satellite. It is clearly important that we learn as much as possible from those failures that do occur. The following pages examine the roles that formal methods might play in the analysis of software failures in space missions
Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback
Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector
Named Models in Coalgebraic Hybrid Logic
Hybrid logic extends modal logic with support for reasoning about individual
states, designated by so-called nominals. We study hybrid logic in the broad
context of coalgebraic semantics, where Kripke frames are replaced with
coalgebras for a given functor, thus covering a wide range of reasoning
principles including, e.g., probabilistic, graded, default, or coalitional
operators. Specifically, we establish generic criteria for a given coalgebraic
hybrid logic to admit named canonical models, with ensuing completeness proofs
for pure extensions on the one hand, and for an extended hybrid language with
local binding on the other. We instantiate our framework with a number of
examples. Notably, we prove completeness of graded hybrid logic with local
binding
Carnap: an Open Framework for Formal Reasoning in the Browser
This paper presents an overview of Carnap, a free and open framework for the development of formal reasoning applications. Carnap’s design emphasizes flexibility, extensibility, and rapid prototyping. Carnap-based applications are written in Haskell, but can be compiled to JavaScript to run in standard web browsers. This combination of features makes Carnap ideally suited for educational applications, where ease-of-use is crucial for students and adaptability to different teaching strategies and classroom needs is crucial for instructors. The paper describes Carnap’s implementation, along with its current and projected pedagogical applications
An ontology for software component matching
The Web is likely to be a central platform for software development in the future. We investigate how Semantic Web technologies, in particular ontologies, can be utilised to support software component development in a Web environment. We use description logics, which underlie Semantic Web ontology languages such as DAML+OIL, to develop
an ontology for matching requested and provided components. A link between modal logic and description logics will prove invaluable for the provision of reasoning support for component and service behaviour
Recommended from our members
Preservation of Community College Logic: Organizational Responses to State Policies and Funding Practices in Three States
Formal models, usability and related work in IR (editorial for special edition)
The Glasgow IR group has carried out both theoretical and empirical work, aimed at giving end users efficient and effective access to large collections of multimedia data
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fast computational model providing fuzzy logic and supervised learning is one
of the main challenges in the machine learning. In this research paper, we
present FSL-BM algorithm as an efficient solution of supervised learning with
fuzzy logic processing using binary meta-feature representation using Hamming
Distance and Hash function to relax assumptions. While many studies focused on
reducing time complexity and increasing accuracy during the last decade, the
novel contribution of this proposed solution comes through integration of
Hamming Distance, Hash function, binary meta-features, binary classification to
provide real time supervised method. Hash Tables (HT) component gives a fast
access to existing indices; and therefore, the generation of new indices in a
constant time complexity, which supersedes existing fuzzy supervised algorithms
with better or comparable results. To summarize, the main contribution of this
technique for real-time Fuzzy Supervised Learning is to represent hypothesis
through binary input as meta-feature space and creating the Fuzzy Supervised
Hash table to train and validate model.Comment: FICC201
Convolution, Separation and Concurrency
A notion of convolution is presented in the context of formal power series
together with lifting constructions characterising algebras of such series,
which usually are quantales. A number of examples underpin the universality of
these constructions, the most prominent ones being separation logics, where
convolution is separating conjunction in an assertion quantale; interval
logics, where convolution is the chop operation; and stream interval functions,
where convolution is used for analysing the trajectories of dynamical or
real-time systems. A Hoare logic is constructed in a generic fashion on the
power series quantale, which applies to each of these examples. In many cases,
commutative notions of convolution have natural interpretations as concurrency
operations.Comment: 39 page
- …