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Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction

Abstract : 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.
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Preprints, Working Papers, ...
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Contributor : Manuel Lopes Connect in order to contact the contributor
Submitted on : Tuesday, March 11, 2014 - 12:20:18 PM
Last modification on : Friday, March 25, 2022 - 3:34:01 PM

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  • HAL Id : hal-00957930, version 1
  • ARXIV : 1403.1497



Manuel Lopes, Luis Montesano. Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction. 2014. ⟨hal-00957930⟩



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