Jul 18, 2020
Agents tackling complex problems in open environments often benefit from the ability to construct knowledge. Learning to independently solve sub-tasks and form models of the world can help agents progress in solving challenging problems. In this talk, we draw attention to challenges that arise when evaluating an agent’s knowledge, specifically focusing on methods that express an agent’s knowledge as predictions. Using the General Value Function framework we highlight the distinction between useful knowledge and strict measures of accuracy. Having identified challenges in assessing an agent’s knowledge, we propose a possible evaluation approach that is compatible with large and open worlds.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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