Representing and Reasoning with Defaults for Learning Agents
Benjamin N. Grosof
AAAI-SS 1993
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.
Benjamin N. Grosof
AAAI-SS 1993
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021
Ran Iwamoto, Kyoko Ohara
ICLC 2023
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019