Compression Progress, Pseudorandomness, & Hyperbolic Discounting
Venue
The Third Conference on Artificial General Intelligence, Atlantis Press, http://www.atlantis-press.com (2010), pp. 186-187
Publication Year
2010
Authors
BibTeX
Abstract
General intelligence requires open-ended exploratory learning. The principle of
compression progress proposes that agents should derive intrinsic reward from
maximizing "interestingness", the first derivative of compression progress over the
agent's history. Schmidhuber posits that such a drive can explain "essential
aspects of ... curiosity, creativity, art, science, music, [and] jokes", implying
that such phenomena might be replicated in an artificial general intelligence
programmed with such a drive. I pose two caveats: 1) as pointed out by Rayhawk, not
everything that can be considered "interesting" according to this definition is
interesting to humans; 2) because of (irrational) hyperbolic discounting of future
rewards, humans have an additional preference for rewards that are structured to
prevent premature satiation, often superseding intrinsic preferences for
compression progress.
