基本信息
浏览量:571
职业迁徙
个人简介
RESEARCH
Professor Mahadevan's research interests span several subfields of artificial intelligence and computer science, including machine learning, multi-agent systems, planning, perception, and robotics. His research in machine learning has been eclectic, ranging from pioneering work in explanation-based learning where his thesis introduced the model of learning apprentices for knowledge acquisition from experts, to the first rigorous study of concept learning with prior determination knowledge using the framework of Probably Approximately Correct (PAC) learning. Over the past decade, his research has centered around a general framework for autonomous learning and sequential decision-making, which studies how agents embedded in real-world environments can acquire knowledge on how to act from a stream of noisy percepts. The framework is rigorously validated using temporal statistical process models, principally Markov decision processes. His recent research has focused on hierarchical probabilistic models, including hierarchical hidden Markov processes, semi-Markov decision processes, and hierarchical partially observable Markov decision processes. Professor Mahadevan has also developed state-of-the-art applications, including mobile robot navigation in indoor office environments, an active vision system for finding objects in cluttered rooms, and coordination among teams of factory agents optimizing production control.
Professor Mahadevan's research interests span several subfields of artificial intelligence and computer science, including machine learning, multi-agent systems, planning, perception, and robotics. His research in machine learning has been eclectic, ranging from pioneering work in explanation-based learning where his thesis introduced the model of learning apprentices for knowledge acquisition from experts, to the first rigorous study of concept learning with prior determination knowledge using the framework of Probably Approximately Correct (PAC) learning. Over the past decade, his research has centered around a general framework for autonomous learning and sequential decision-making, which studies how agents embedded in real-world environments can acquire knowledge on how to act from a stream of noisy percepts. The framework is rigorously validated using temporal statistical process models, principally Markov decision processes. His recent research has focused on hierarchical probabilistic models, including hierarchical hidden Markov processes, semi-Markov decision processes, and hierarchical partially observable Markov decision processes. Professor Mahadevan has also developed state-of-the-art applications, including mobile robot navigation in indoor office environments, an active vision system for finding objects in cluttered rooms, and coordination among teams of factory agents optimizing production control.
研究兴趣
论文共 211 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
International Journal of Innovative Science and Research Technology (IJISRT)pp.593-595, (2024)
CoRR (2023)
引用24浏览0EI引用
24
0
arXiv (Cornell University) (2023)
引用5浏览0EI引用
5
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn