  In recent years, Artificial Intelligence (AI) systems have surpassed human
intelligence in a variety of computational tasks. However, AI systems, like
humans, make mistakes, have blind spots, hallucinate, and struggle to
generalize to new situations. This work explores whether AI can benefit from
creative decision-making mechanisms when pushed to the limits of its
computational rationality. In particular, we investigate whether a team of
diverse AI systems can outperform a single AI in challenging tasks by
generating more ideas as a group and then selecting the best ones. We study
this question in the game of chess, the so-called drosophila of AI. We build on
AlphaZero (AZ) and extend it to represent a league of agents via a
latent-conditioned architecture, which we call AZ_db. We train AZ_db to
generate a wider range of ideas using behavioral diversity techniques and
select the most promising ones with sub-additive planning. Our experiments
suggest that AZ_db plays chess in diverse ways, solves more puzzles as a group
and outperforms a more homogeneous team. Notably, AZ_db solves twice as many
challenging puzzles as AZ, including the challenging Penrose positions. When
playing chess from different openings, we notice that players in AZ_db
specialize in different openings, and that selecting a player for each opening
using sub-additive planning results in a 50 Elo improvement over AZ. Our
findings suggest that diversity bonuses emerge in teams of AI agents, just as
they do in teams of humans and that diversity is a valuable asset in solving
computationally hard problems.
