  As open-ended learning based on divergent search algorithms such as Novelty
Search (NS) draws more and more attention from the research community, it is
natural to expect that its application to increasingly complex real-world
problems will require the exploration to operate in higher dimensional Behavior
Spaces which will not necessarily be Euclidean. Novelty Search traditionally
relies on k-nearest neighbours search and an archive of previously visited
behavior descriptors which are assumed to live in a Euclidean space. This is
problematic because of a number of issues. On one hand, Euclidean distance and
Nearest-neighbour search are known to behave differently and become less
meaningful in high dimensional spaces. On the other hand, the archive has to be
bounded since, memory considerations aside, the computational complexity of
finding nearest neighbours in that archive grows linearithmically with its
size. A sub-optimal bound can result in "cycling" in the behavior space, which
inhibits the progress of the exploration. Furthermore, the performance of NS
depends on a number of algorithmic choices and hyperparameters, such as the
strategies to add or remove elements to the archive and the number of
neighbours to use in k-nn search. In this paper, we discuss an alternative
approach to novelty estimation, dubbed Behavior Recognition based Novelty
Search (BR-NS), which does not require an archive, makes no assumption on the
metrics that can be defined in the behavior space and does not rely on nearest
neighbours search. We conduct experiments to gain insight into its feasibility
and dynamics as well as potential advantages over archive-based NS in terms of
time complexity.
