Modelling serendipity in a computational context
  The term serendipity describes a creative process that develops, in context,
with the active participation of a creative agent, but not entirely within that
agent's control. While a system cannot be made to perform serendipitously on
demand, we argue that its $\mathit{serendipity\ potential}$ can be increased by
means of a suitable system architecture and other design choices. We distil a
unified description of serendipitous occurrences from historical theorisations
of serendipity and creativity. This takes the form of a framework with six
phases: $\mathit{perception}$, $\mathit{attention}$, $\mathit{interest}$,
$\mathit{explanation}$, $\mathit{bridge}$, and $\mathit{valuation}$. We then
use this framework to organise a survey of literature in cognitive science,
philosophy, and computing, which yields practical definitions of the six
phases, along with heuristics for implementation. We use the resulting model to
evaluate the serendipity potential of four existing systems developed by
others, and two systems previously developed by two of the authors. Most
existing research that considers serendipity in a computing context deals with
serendipity as a service; here we relate theories of serendipity to the
development of autonomous systems and computational creativity practice. We
argue that serendipity is not teleologically blind, and outline representative
directions for future applications of our model. We conclude that it is
feasible to equip computational systems with the potential for serendipity, and
that this could be beneficial in varied computational creativity/AI
applications, particularly those designed to operate responsively in real-world
contexts.
