  Planning is an extraordinary ability in which the brain imagines and then
enacts evaluated possible futures. Using traditional planning models, computer
scientists have attempted to replicate this capacity with some level of success
but ultimately face a reoccurring limitation: as the plan grows in steps, the
number of different possible futures makes it intractable to determine the
right sequence of actions to reach a goal state. Based on prior theoretical
work on how the ecology of an animal governs the value of spatial planning, we
developed a more efficient biologically-inspired planning algorithm, TLPPO.
This algorithm allows us to achieve mouselevel predator evasion performance
with orders of magnitude less computation than a widespread algorithm for
planning in the situations of partial observability that typify predator-prey
interactions. We compared the performance of a real-time agent using TLPPO
against the performance of live mice, all tasked with evading a robot predator.
We anticipate these results will be helpful to planning algorithm users and
developers, as well as to areas of neuroscience where robot-animal interaction
can provide a useful approach to studying the basis of complex behaviors.
