报告简介:
Answer set programming (ASP) grows out of the investigation of logic programming and knowledge representation. ASP and its extensions offer elegant and effective declarative solutions to many fundamental problems in Artificial Intelligence. In this talk, we will discuss how ASP and its extensions are used to naturally represent planning problems, constraint problems and probabilistic problems.
报告人简介:
Yuanlin Zhang Obtained his PhD degree from National University of Singapore. His research interests are in designing declarative programming languages, developing efficient inference algorithms for these languages, and applying these programming systems to challenging problems in Artificial Intelligence and application domains including health care, robots and education. He has published more than over 30 high quality papers in international conferences and journals, such as Artificial Intelligence Journal, Theory and Practice of Logic Programming, Computational Intelligence, Journal of Artificial Intelligence Research, AAAI, IJCAI.