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图灵奖得主Joseph Sifakis教授讲座

阅读数:134    发布:2019-01-10 16:23    

报告题目:Autonomous Systems – A Rigorous Architectural Characterization(自主系统 – 严谨的架构描述)

报告时间:1月14日(周一)下午3:00—4:00

报告地点:计算机与软件学院938会议室

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Joseph Sifakis教授简介

Joseph Sifakis教授是2007 年图灵奖得主、欧洲科学院院士、法国科学院院士、美国文理科学院院士、美国国家工程院院士、IEEE Fellow、ACM Fellow。他是法国Verimag国家实验室创始人、法国格拉诺布尔大学教授、智能软件与系统研究中心主任,主要研究成果和贡献包括模型检测和严密系统设计。2007年,Joseph Sifakis教授被授予了图灵奖,以表彰其在模型检查理论和应用方面做出的卓越贡献。如今,该模型检查理论已经成为了使用最广泛的系统验证技术。他曾获得法国国家功勋大臣勋章、法国国家荣誉将军勋章、希腊国家凤凰勋章以及希腊国家荣誉将军勋章,并于2012年获得了达芬奇奖章。他还是欧洲Synesys、ASTUS、Kalray、ISD等多家公司的创始人。

报告摘要:

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.

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深圳大学计算机与软件学院 2009-2016