Why Open-Endedness Matters. Stanley, K. O. Artificial Life, 25(3):232–235, 2019. ZSCC: 0000011 doi abstract bibtex Rather than acting as a review or analysis of the field, this essay focuses squarely on the motivations for investigating open-endedness and the opportunities it opens up. It begins by contemplating the awesome accomplishments of evolution in nature and the profound implications if such a process could be ignited on a computer. Some of the milestones in our understanding so far are then discussed, finally closing by highlighting the grand challenge of formalizing open-endedness as a computational process that can be encoded as an algorithm. The main contribution is to articulate why open-endedness deserves a place alongside artificial intelligence as one of the great computational challenges, and opportunities, of our time.
@article{stanley_why_2019,
title = {Why {Open}-{Endedness} {Matters}},
volume = {25},
issn = {1530-9185},
doi = {10.1162/artl_a_00294},
abstract = {Rather than acting as a review or analysis of the field, this essay focuses squarely on the motivations for investigating open-endedness and the opportunities it opens up. It begins by contemplating the awesome accomplishments of evolution in nature and the profound implications if such a process could be ignited on a computer. Some of the milestones in our understanding so far are then discussed, finally closing by highlighting the grand challenge of formalizing open-endedness as a computational process that can be encoded as an algorithm. The main contribution is to articulate why open-endedness deserves a place alongside artificial intelligence as one of the great computational challenges, and opportunities, of our time.},
language = {eng},
number = {3},
journal = {Artificial Life},
author = {Stanley, Kenneth O.},
year = {2019},
pmid = {31397603},
note = {ZSCC: 0000011 },
keywords = {Algorithms, Artificial Intelligence, Biological Evolution, Computational Biology, Models, Theoretical, Open-endedness, artificial intelligence, machine learning, novelty search, open-ended algorithms, open-ended evolution, quality diversity},
pages = {232--235},
}
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