Lifelong Machine Learning Systems: Beyond Learning Algorithms 论文

摘要

Lifelong Machine Learning, or LML, considers sys-tems that can learn many tasks from one or more do-mains over its lifetime. The goal is to sequentially re-tain learned knowledge and to selectively transfer that knowledge when learning a new task so as to develop more accurate hypotheses or policies. Following a re-view of prior work on LML, we propose that it is now appropriate for the AI community to move beyond learning algorithms to more seriously consider the na-ture of systems that are capable of learning over a life-time. Reasons for our position are presented and poten-tial counter-arguments are discussed. The remainder of the paper contributes by defining LML, presenting a ref-erence framework that considers all forms of machine learning, and listing several key challenges for and ben-efits from LML research. We conclude with ideas for next steps to advance the field.