“高屋建瓴AI公开课”第11期:Decision Making and Reinforcement Learning
(高瓴人工智能学院发布于:2021-12-02 11:43:04)

 

       “高屋建瓴AI公开课”项目由中国人民大学高瓴人工智能学院发起,旨在扩大人工智能学科影响力、提升学科发展水准。公开课项目命名为“高屋建瓴”,寓意在高瓴人工智能学院的平台上,汇聚高端人才,发出人工智能研究方向高瞻远瞩的声音。

 

 

讲座主题:

Decision Making and Reinforcement Learning

讲座时间:12月1日(周三) 16:00-17:20

腾讯线上会议ID:742-740-515

 

活动安排:

16:00-16:10  主持人介绍

16:10-16:50  讲座

16:50-17:20  Q&A

 

讲座摘要

Artificial Intelligence (AI) aims at creating a computerised system capable of acquiring and applying knowledge and skills as a result of experience. Being able to perceive the world and subsequently interacting with it are the two pillars of intelligence that should be maintained in any AI system. 

In scientific research, it is widely accepted that the former corresponds to pattern recognition, while the latter is related to machine decision making. With the decades of efforts to supervised and unsupervised learning, many pattern recognition problems have been well explored and a lot of successful stories can be found ranging from speech recognition and machine translation to visual object detection and recognition. By contrast, machine decision making is still at its infant stage due to lack of understanding of its intrinsic complexity of the decision space, and more research effort is required to drive progress in this field forward. 

In addition, multi-agent learning arises in a variety of domains where intelligent agents interact not only with the (unknown) environment but also with each other. It has an increasing number of applications ranging from controlling a group of autonomous vehicles/robots/drones to coordinating collaborative bots in production lines, optimising distributed sensor networks/traffic, and machine bidding in competitive e-commerce, search and information retrieval and financial markets.

 

In this talk, I shall provide an up-to-date introduction on the technique and algorithms of machine decision making and multi-agent AI, with a focus on competition, collaboration, and communications among intelligent agents. The studies in both game theory and machine learning will be examined in a unified treatment. I shall also sample our recent work on the subject including mean-field multiagent reinforcement learning, generative adversarial nets, hierarchical reinforcement learning and their applications in information retrieval and search.  


主讲嘉宾

Prof. Jun Wang, UCL

Jun Wang is Professor at the Computer Science department, University College London. Prof. Jun Wang\'s main research interests are in the areas of AI and intelligent systems, covering (multiagent) reinforcement learning, deep generative models, and their diverse applications on information retrieval, recommender systems and personalization, data mining, smart cities, bot planning, and computational advertising.

 His team won the first global real-time bidding algorithm contest with 80+ participants worldwide. Jun has published over 200 research papers and is a winner of multiple Best Paper awards. He was a recipient of the Beyond Search --- Semantic Computing and Internet Economics award by Microsoft Research and also received Yahoo! FREP Faculty award. He has served as an Area Chair in ACM CIKM and ACM SIGIR. His recent service includes co-chair of Artificial Intelligence, Semantics, and Dialog in ACM SIGIR 2018.

 

邀请人

陈旭  高瓴人工智能学院准聘助理教授

 

       陈旭博士毕业于清华大学,博士期间曾在佐治亚理工学院进行交流访问,博士毕业后曾在英国伦敦大学学院担任博士后研究员。于2020年加入中国人民大学,任助理教授。其主要研究方向为推荐系统,强化学习,因果推断等。曾在SIGIR、TOIS、WWW、WSDM、CIKM、AAAI等信息检索领域顶级会议和期刊发表论文40余篇。据Google Scholar 统计,已发表论文共计被引用2000余次。曾获得The Web Conference 2018 最佳论文提名奖、AIRS 2017 最佳论文奖。陈旭曾担任SIGIR、WWW、IJCAI、AAAI、CIKM等会议的程序委员会委员,以及TOIS、TKDE、TIST、JMLR等杂志的审稿人。




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