Turing Award laureate Joseph Sifakis delivered a keynote speech at Master Forum, CUHK-Shenzhen on Oct. 19, 2018. He elaborated on Autonomous Systems – A Rigorous Architectural Characterization.  "We lack a rigorous common semantic framework for autonomous systems. "  He believes that autonomy is a kind of broad intelligence and building trustworthy and optimal autonomous systems goes far beyond the AI challenge.  Prof. Joseph Sifakis is Emeritus Senior CNRS Researcher at Verimag.  He is a member of the French Academy of Sciences, a member of the French National Academy of Engineering. In 2007, he received the Turing Award for his contribution to the theory and application of model checking. The Master Forum took place at Governing Board Meeting Room, Dao Yuan Building， CUHK-Shenzhen.  Prof. Chen Changwen, Dean of the School of Science and Engineering, Prof. Baoting Zhu,  Associate Dean of the School of Science and Engineering, faculty members and students attended the event.

Prof. Joseph Sifakis

【Speaker Profile】

Joseph Sifakis, Turing Award Winner, is the founder and Emeritus Senior CNRS Researcher at Verimag laboratory in Grenoble. His current research interests cover fundamental and applied aspects of system design. The main focus of his work is on the formalization of system design as a process leading from given requirements to trustworthy, optimized and correct-by-construction implementations. 

Joseph Sifakis has been a full professor at Ecole Polytechnique Fédérale de Lausanne (EPFL) for the period 2011-2016. 

 In 2007, he received the Turing Award for his contribution to the theory and application of model checking, the most widely used system verification technique today.

 Joseph Sifakis is a member of the French Academy of Sciences, a member of the French National Academy of Engineering, a member of Academia Europea, a member of the American Academy of Arts and Sciences, and a member of the National Academy of Engineering. He is a Grand Officer of the French National Order of Merit, a Commander of the French Legion of Honor. He has received the Leonardo da Vinci Medal in 2012.

【Abstract】

The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. There is currently a lot of confusion regarding the main characteristics of autonomous systems. In the literature, we find a profusion of poorly understood “self”-prefixed terms related to autonomy such as Self-healing, Self-optimization, Self-protection, Self-awareness, Self-organization etc. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues.

Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic feature: perception, reflection, goal management, planning and self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems.

We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. A main conclusion is that autonomy should be associated with functionality and not with specific techniques. Machine learning is essential for autonomy although it can meet only a small portion of the needs implied by autonomous system design.

We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.