A Context-Sensitive and User-Centric Approach to Developing Personal Assistants

Several ongoing projects in the MAPLE (Multi-Agent Planning and LEarning) lab at UMBC and the Machine Learning Systems Group at JPL focus on problems that we view as central to the development of persistent agents. This position paper describes our current research in this area, focusing on four topics in particular: effective use of observational and active learning, utilizing repeated behavioral contexts, clustering with annotated constraints, and learning user preferences.