  The field of evolutionary computation is inspired by the achievements of
natural evolution, in which there is no final objective. Yet the pursuit of
objectives is ubiquitous in simulated evolution. A significant problem is that
objective approaches assume that intermediate stepping stones will increasingly
resemble the final objective when in fact they often do not. The consequence is
that while solutions may exist, searching for such objectives may not discover
them. This paper highlights the importance of leveraging human insight during
search as an alternative to articulating explicit objectives. In particular, a
new approach called novelty-assisted interactive evolutionary computation
(NA-IEC) combines human intuition with novelty search for the first time to
facilitate the serendipitous discovery of agent behaviors. In this approach,
the human user directs evolution by selecting what is interesting from the
on-screen population of behaviors. However, unlike in typical IEC, the user can
now request that the next generation be filled with novel descendants. The
experimental results demonstrate that combining human insight with novelty
search finds solutions significantly faster and at lower genomic complexities
than fully-automated processes, including pure novelty search, suggesting an
important role for human users in the search for solutions.
