报告题目：协同演化的学习机制与能力

报 告 人：姚新 南方科技大学计算机与工程系

报告时间：2018年12月17日 周一 上午10:00-11:00

报告地点：主楼III-401

报告摘要：人工智能是一个十分丰富的研究领域。各种学习方法各有其长处和实用场景。本讲座通过一个简单的例子，重复的囚徒困境博弈，来说明协同演化在新一代机器学习机制中的重要性。重复的囚徒困境博弈（iterated prisoner's dilemma, IPD）已经被广泛的用于模拟多样的现实生活场景。我们通过这个例子来展示演化学习在这一场景下的重要性和不可替代性。对于各种复杂的IPD博弈，协同演化学习都能自动地学到最佳策略。我们在讲座中还简要介绍一个缜密的、可以量化的测量协同演化学习的泛化能力的理论构架。最后，我们给出一个协同演化水下机器人的控制系统与形态的例子。

报告人简介：

 Xin Yao is a Chair Professor of Computer Science at the Southern University of
Science and Technology, Shenzhen, China, and a part-time professor at School of
Computer Science, the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He received the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013