  This paper proposes a novel method to identify unexpected structures in 2D
floor plans using the concept of Bayesian Surprise. Taking into account that a
person's expectation is an important aspect of the perception of space, we
exploit the theory of Bayesian Surprise to robustly model expectation and thus
surprise in the context of building structures. We use Isovist Analysis, which
is a popular space syntax technique, to turn qualitative object attributes into
quantitative environmental information. Since isovists are location-specific
patterns of visibility, a sequence of isovists describes the spatial perception
during a movement along multiple points in space. We then use Bayesian Surprise
in a feature space consisting of these isovist readings. To demonstrate the
suitability of our approach, we take "snapshots" of an agent's local
environment to provide a short list of images that characterize a traversed
trajectory through a 2D indoor environment. Those fingerprints represent
surprising regions of a tour, characterize the traversed map and enable indoor
LBS to focus more on important regions. Given this idea, we propose to use
"surprise" as a new dimension of context in indoor location-based services
(LBS). Agents of LBS, such as mobile robots or non-player characters in
computer games, may use the context surprise to focus more on important regions
of a map for a better use or understanding of the floor plan.
