Computational
rationality: A converging paradigm for intelligence in brains, minds, and
machines
Samuel
J. Gershman, Eric J. Horvitz, Joshua B. Tenenbaum
Reprint (pdf) Full text at Science
Abstract:
After growing up
together, and mostly growing apart in the second half of the 20th century, the
fields of artificial intelligence (AI), cognitive science, and neuroscience are
reconverging on a shared view of the computational
foundations of intelligence that promotes valuable cross-disciplinary exchanges
on questions, methods, and results. We chart advances over the past several
decades that address challenges of perception and action under uncertainty
through the lens of computation. Advances include the development of
representations and inferential procedures for large-scale probabilistic
inference and machinery for enabling reflection and decisions about tradeoffs
in effort, precision, and timeliness of computations. These tools are deployed
toward the goal of computational rationality: identifying decisions with
highest expected utility, while taking into consideration the costs of
computation in complex real-world problems in which most relevant calculations
can only be approximated. We highlight key concepts with examples that show the
potential for interchange between computer science, cognitive science, and
neuroscience.