124 citations · 311 across the 12 of their papers we have counts for
4 papers · 1 filter
AI Finds A Way
Aaron Dharna, Cong Lu, Ryan Sullivan +3
Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study them. They often astonish pract…
Evolution and The Knightian Blindspot of Machine Learning
Joel Lehman, Elliot Meyerson, Tarek El-Gaaly +2
This paper claims that machine learning (ML) largely overlooks an important facet of general intelligence: robustness to a qualitatively unknown future in an open world. Such robus…
OMNI: Open-endedness via Models of human Notions of Interestingness
Jenny Zhang, Joel Lehman, Kenneth Stanley +1
Open-ended algorithms aim to learn new, interesting behaviors forever. That requires a vast environment search space, but there are thus infinitely many possible tasks. Even after…
Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior
Jacob Schrum, Joel Lehman, Sebastian Risi
An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in prac…