Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Researchers are developing domain-driven data mining techniques that target actionable knowledge discovery (KDD) in complex domain problems. The domain-driven technique aims to utililize and mine many aspects of intelligence, such as in-depth data, domain expertise, real-time human involvement, process, environment, and social intelligence. It also metasynthesizes its intelligence sources for actionable knowledge discovery. The method works to expose next-generation methodologies for actionable knowledge discovery, identifying ways in which KDD can better contribute to critical domain problems in theory and practice. It undercovers domain-driven techniques to help KDD, strengthen business intelligence in complex enterprise applications. It also reveals applications that effectively deploy domain-driven data mining method,to solve complex practical problems.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Shashank Ahire, Melissa Guyre, et al.
CUI 2025
Arnold.L. Rosenberg
Journal of the ACM