Active Learning for Autonomous Intelligent Agents: Exploration,
  Curiosity, and Interaction
  In this survey we present different approaches that allow an intelligent
agent to explore autonomous its environment to gather information and learn
multiple tasks. Different communities proposed different solutions, that are in
many cases, similar and/or complementary. These solutions include active
learning, exploration/exploitation, online-learning and social learning. The
common aspect of all these approaches is that it is the agent to selects and
decides what information to gather next. Applications for these approaches
already include tutoring systems, autonomous grasping learning, navigation and
mapping and human-robot interaction. We discuss how these approaches are
related, explaining their similarities and their differences in terms of
problem assumptions and metrics of success. We consider that such an integrated
discussion will improve inter-disciplinary research and applications.
