The advent of systems that have strong AI/Robotics components will bring dramatic changes to our society. Such systems, also known as Systems 4.0, are required to operate in increasingly complex, open, distributed, dynamic, heterogeneous, and highly interactive application environments. In order to do so, systems should encompass several dynamically interacting components, each with their own thread of control, and engaging in complex coordination and cooperation protocols.
The adoption of Systems 4.0 changes everything. In industry, it will lead to what some people call the 4th industrial revolution. The impact does not limit to industry, though. Systems 4.0 also have the potential of improve our lives. The prospect of highly interacting and intelligent environments has also the potential of coping with disabilities or impairments that humans can have.
Despite the advances in technologies, designing and deploying such systems is not trivial. It may require the collaboration of multidisciplinary groups with a strong core of AI experts. The tools for successfully collaborating and producing Systems 4.0-like solutions have to do with Software Engineering in general and Software Engineering methodologies in particular. Traditionally, agent literature has produced a number of Software Engineering methodologies, namely Agent Oriented Methodologies, which bring AI concerns together with Software Engineering concerns. Knowledge based methodologies have been successful too in addressing the needs of strong AI systems. However, existing methodologies cannot remain ignorant of recent AI advances. For instance, modern results from artificial neural network approaches, such as deep learning, need to be considered and integrated into current engineering practices.
Advances do not limit to new technologies. The way AI is applied matters too. The form of the AI solution depends strongly also on what kind of AI the end-user is willing to accept. Also, ethical principles ought to be considered before the inception of the system. What kind of data is acceptable to use and to what decisions the system is entitled to make is a growing concern in the AI community. This is especially true when there are autonomous cars that need to interact with human drivers of other non-autonomous vehicles. Avoiding an accident may involve quick decisions that may endanger others.
The purpose of this thematic track is to provide a high-profile, internationally respected discussion forum on the most recent and innovative scientific research in the areas of Software Engineering and AI Systems. SE4AIS 2017 will appeal to any researcher and practitioner who is interested in the theoretical and practical aspects of Software Engineering and Artificial Intelligence, including Agent and Multi-Agent Software Engineering. Both theoretical and practical research should be situated in the context of existing or new methodologies for developing AI systems. Contributions with emphasis on theoretical issues should make clear the significance and relevance of those results to the community, while more practical subjects should clearly relate the work with the state of the art and make clear their contribution in other general applications.
In addition to paper presentations, the program will include keynote speakers and possibly demo presentations.
Topics of Interest
Some topics of special interest are:
- Methodologies for AI based systems analysis and design
- Agent-oriented requirements analysis and specification
- UML and AI based systems;
- Model-driven architecture (MDA) for AI Based systems
- Service-oriented computing in the context of AI based systems
- Verification and validation techniques for AI based systems
- Software development environments and CASE tools for AI based systems
- Formal methods for AI based systems, including specification and verification logics
- Model checking for AI based systems
- Engineering of large-scale AI based systems
- Engineering Open AI Ecosystems
- Standardisations for AI based systems
- Re-use approaches for AI based systems, including design patterns, frameworks, components, and architectures
- Implications of AI based systems on organizational and social structures within and between companies (e.g. changes in roles, responsibilities, transparency, business processes and decision schemes)
- Practical applications of AI systems to specific domains, such as Ambient Assisted Living, Industry 4.0 or Smart X (Smart cities, Smart grids, ..), using engineering principles
- Responsible Research and Innovation applied to the engineering of AI systems
- Ethical principles applied to the engineering of AI systems
All papers should be submitted in PDF format through EPIA 2017 submission Website (select “Software Engineering for Autonomous and Intelligent Systems” track).
Submissions must be original and can be of two types: full papers should not exceed twelve (12) pages in length, whereas short papers should not exceed six (6) pages.
Papers must adhere to the formatting instructions of the conference. Each submission will be peer reviewed by at least three members of the Program Committee. The reviewing process is double blind, so authors should remove names and affiliations from the submitted papers, and must take reasonable care to assure anonymity during the review process.
Ana Paula Rocha, FEUP / LIACC, Porto, Portugal
António J. M. Castro, LIACC, Porto, Portugal
Pavlos Moraitis, LIPADE, Paris Descartes University, France
Jorge Jesus Gomez-Sanz, U. Complutense de Madrid, Spain
Daniel Silva, University of Porto, Portugal
Francisco Garijo, Universidad Complutense de Madrid, Spain
Franco Zambonelli, University of Modena and Reggio Emilia, Italy
Frédéric Migeon, IRIT, University of Toulouse III, France
Juan Botia, King’s College London, United Kingdom
Juan Garcia-Ojeda, Universidad Santo Tomás, Colombia
Holder Giese, University of Potsdam, Germany
Laszlo Gulyas, AITIA International Inc, Hungary
Massimo Conssentino, ICAR-CNR, Italy
Medhi Dastani, University of Utrecht, The Netherlands
Michael Winikoff, University of Otago, New Zealand
Nikolaos Spanoudakis, Technical University of Crete, Greece
Ruben Fuentes, Universidad Complutense de Madrid, Spain
Rui Maranhão, Instituto Superior Técnico, Portugal
Tom Holvoet, KU Leuven, Belgium
Vincent Hilaire, Belfort-Montbeliard Technology University, France