Formal presentation of fuzzy systems with multiple sensor inputs

The paper addresses the problems of complexity in fuzzy rule based systems with multiple sensor inputs. The number of fuzzy rules in this case is an exponential function of the number of inputs. Some of the existing methods for rule base reductions are reviewed and their drawbacks summarized. As an alternative, a novel methodology for complexity management in fuzzy systems is presented which is based on formal presentation techniques such as integer tables. A Matlab example is shown illustrating the presentation of a fuzzy rule base with an integer table. Finally, some future research directions are outlined within the framework of the proposed methodology

Formal methods for industrial critical systems, preface to the special section

[EN] This special issue contains improved versions of selected papers from the workshops on Formal Methods for Industrial Critical Systems (FMICS) held in Eindhoven, The Netherlands, in November 2009 and in Antwerp, Belgium, in September 2010. These were, respectively, the 14th and 15th of a series of international workshops organized by an open working group supported by ERCIM (European Research Consortium for Informatics and Mathematics) that promotes research in all aspects of formal methods (see details in http://www.inrialpes.fr/vasy/fmics/). The FMICS workshops that have produced this special issue considered papers describing original, previously unpublished research and not simultaneously submitted for publication elsewhere, and dealing with the following themes: Design, specification, code generation and testing based on formal methods. Methods, techniques and tools to support automated analysis, certification, debugging, learning, optimization and transformation of complex, distributed, real-time and embedded systems. Verification and validation methods that address shortcomings of existing methods with respect to their industrial applicability (e.g., scalability and usability issues). Tools for the development of formal design descriptions. Case studies and experience reports on industrial applications of formal methods, focusing on lessons learned or new research directions. Impact and costs of the adoption of formal methods. Application of formal methods in standardization and industrial forums. The selected papers are the result of several evaluation steps. In response to the call for papers, FMICS 2009 received 24 papers and FMICS 2010 received 33 papers, with 10 and 14 accepted, respectively, which were published by Springer- Verlag in the series Lecture Notes in Computer Science (volumes 5825 [1] and 6371 [2]). Each paper was reviewed by at least three anonymous referees which provided full written evaluations. After the workshops, the authors of 10 papers were invited to submit extended journal versions to this special issue. These papers passed two review phases, and finally 7 were accepted to be included in the journal.his work has been partially supported by the EU (FEDER) and the Spanish MEC TIN2010-21062-C02-02 project, MICINN INNCORPORA-PTQ program, and by Generalitat Valenciana, ref. PROMETEO2011/052.Alpuente Frasnedo, M.; Joubert ., C.; Kowalewski, S.; Roveri, M. (2013). Formal methods for industrial critical systems, preface to the special section. Science of Computer Programming. 78(7):775-777. doi:10.1016/j.scico.2012.05.005S77577778

A Review of Formal Methods applied to Machine Learning

We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems

Generating realistic scaled complex networks

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the paper was presented at the 5th International Workshop on Complex Networks and their Application

A Unifying Framework for Learning Argumentation Semantics

Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Formal Methods in Dependable Systems Engineering : A Survey of Professionals from Europe and North America

Context: Formal methods (FMs) have been around for a while, still being unclear how to leverage their benefits, overcome their challenges, and set new directions for their improvement towards a more successful transfer into practice. Objective: We study the use of formal methods in mission-critical software domains, examining industrial and academic views. Method: We perform a cross-sectional on-line survey. Results: Our results indicate an increased intent to apply FMs in industry, suggesting a positively perceived usefulness. But the results also indicate a negatively perceived ease of use. Scalability, skills, and education seem to be among the key challenges to support this intent. Conclusions: We present the largest study of this kind so far (N = 216), and our observations provide valuable insights, highlighting directions for future theoretical and empirical research of formal methods. Our findings are strongly coherent with earlier observations by Austin and Parkin (1993)