We are seeking all new formal methods and/or extensions/applications of existing ones for publication in this workshop. All the authors of FMI'19 will be invited to expand and submit their papers for possible publication in a prestigious journal special issue (please visit the conference website for more details).
Formal Methods (FM) are mathematically-based techniques to model, design, and analyze computing systems. Such techniques aim at improving the dependability of computing systems. Different techniques may be required throughout the development life cycle to cover the different aspects of the system.
Heuristics, which depend upon and support machine learning models are of general interest. In particular, AI-based methods for domain-specific heuristic acquisition will be of interest. What do they contribute to the problem-solving process? What do they make solvable, which would not be in their absence? Which among a plethora of techniques for heuristic acquisition are best and why (e.g., See the 1940s book, “How to Solve it,” by Polyga).
Machine learning – including the more-recent, deep neural networks, match human abilities in various tasks. Solutions based on machine learning are ubiquitous, from automated medical diagnosis and self-driving cars to security. However, machine learning does not offer guarantees or reasoning techniques to prove the dependability of the automated decisions. Such systems must be verified/validated using existing techniques or call for new techniques. Formal reasoning techniques are needed to arrive at as well as to explain deductive and even inductive conclusions. Explanation subsystems have served expert systems in this capacity; but, what will fill the gap for deep learning neural networks?
The workshop seeks contributions from researchers and practitioners interested in all aspects of integrated methods, either formal or semi-formal, for system development covering all engineering development phases from user requirements through validation/testing. The workshop encourages contributions from new initiatives building bridges between FM and machine learning – especially contributions using FM as a tool to verify safety-critical machine learning systems.
Moreover, logics for learning and generalization – possibly applied to the use of heuristics either in machine learning or in knowledge discovery are especially welcome.
Submitted papers must be unpublished and not considered elsewhere for publication. Submissions will undergo a rigorous review process handled by the Technical Program Committee. Papers will be selected based on their originality, significance, relevance, and clarity of presentation. Only electronic submissions in PDF format through the EasyChair submission site https://easychair.org/conferences/?conf=fmi2019 will be considered. Papers must be in English, up to 8 pages in IEEE format, including references and appendices. The IEEE LaTeX and Microsoft Word templates, as well as formatting guidelines, can be found on the paper submission instructions available at the main conference website.
At least one of the authors must register and present each accepted paper. Registered and presented papers will be published as workshop papers in the IEEE IRI conference proceedings published by IEEE Computer Society Press and included in the IEEE Xplore Digital Library. As for previous editions of the workshop, authors of best papers will be invited to expand and submit their papers for possible publication in a book or in a journal special issue.
Lydia Bouzar-Benlabiod at firstname.lastname@example.org