Eigenoption discovery through the deep successor representation
Marlos C. Machado, Clemens Rosenbaum, et al.
ICLR 2018
While the Turing test is a well-known method for evaluating machine intelligence, it has a number of drawbacks that make it problematic as a rigorous and practical test for assessing progress in general-purpose AI. For example, the Turing test is deception based, subjectively evaluated, and narrowly focused on language use. We suggest that a test would benefit from including the following requirements: focus on rational behavior, test several dimensions of intelligence, automate as much as possible, score as objectively as possible, and allow incremental progress to be measured. In this article we propose a methodology for designing a test that consists of a series of events, analogous to the Olympic Decathlon, which complies with these requirements. The approach, which we call the I-athlon, is intended ultimately to enable the community to evaluate progress toward machine intelligence in a practical and repeatable way.
Marlos C. Machado, Clemens Rosenbaum, et al.
ICLR 2018
Tian Gao, Kshitij Fadnis, et al.
ICML 2017
Shayegan Omidshafiei, Dong Ki Kim, et al.
AAAI 2019
David Ungar, Sam S. Adams
OOPSLA 2010