  What do humans do when confronted with a common challenge: we know where we
want to go but we are not yet sure the best way to get there, or even if we
can. This is the problem posed to agents during spatial navigation and
pathfinding, and its solution may give us clues about the more abstract domain
of planning in general. In this work, we model pathfinding behavior in a
continuous, explicitly exploratory paradigm. In our task, participants (and
agents) must coordinate both visual exploration and navigation within a
partially observable environment. Our contribution has three primary
components: 1) an analysis of behavioral data from 81 human participants in a
novel pathfinding paradigm conducted as an online experiment, 2) a proposal to
model prospective mental simulation during navigation as particle filtering,
and 3) an instantiation of this proposal in a computational agent. We show that
our model, Active Dynamical Prospection, demonstrates similar patterns of map
solution rate, path selection, and trial duration, as well as attentional
behavior (at both aggregate and individual levels) when compared with data from
human participants. We also find that both distal attention and delay prior to
first move (both potential correlates of prospective simulation) are predictive
of task performance.
