  This paper applies machine learning and the mathematics of chaos to the task
of designing indoor rock-climbing routes. Chaotic variation has been used to
great advantage on music and dance, but the challenges here are quite
different, beginning with the representation. We present a formalized system
for transcribing rock climbing problems, then describe a variation generator
that is designed to support human route-setters in designing new and
interesting climbing problems. This variation generator, termed Strange Beta,
combines chaos and machine learning, using the former to introduce novelty and
the latter to smooth transitions in a manner that is consistent with the style
of the climbs This entails parsing the domain-specific natural language that
rock climbers use to describe routes and movement and then learning the
patterns in the results. We validated this approach with a pilot study in a
small university rock climbing gym, followed by a large blinded study in a
commercial climbing gym, in cooperation with experienced climbers and expert
route setters. The results show that {\sc Strange Beta} can help a human setter
produce routes that are at least as good as, and in some cases better than,
those produced in the traditional manner.
