Inspired by the notion of surprise for unconventional discovery
in computational creativity, we introduce a general
search algorithm we name surprise search. Surprise search is
grounded in the divergent search paradigm and is fabricated
within the principles of metaheuristic (evolutionary) search.
The algorithm mimics the self-surprise cognitive process of
creativity and equips computational creators with the ability
to search for outcomes that deviate from the algorithm’s expected
behavior. The predictive model of expected outcomes
is based on historical trails of where the search has been and
some local information about the search space. We showcase
the basic steps of the algorithm via a problem solving (maze
navigation) and a generative art task. What distinguishes surprise
search from other forms of divergent search, such as the
search for novelty, is its ability to diverge not from earlier and
seen outcomes but rather from predicted and unseen points in
the creative domain considered.This work has been supported in part by the FP7 Marie Curie
CIG project AutoGameDesign (project no: 630665).peer-reviewe
