  The ability to plan ahead efficiently is key for both living organisms and
artificial systems. Model-based planning and prospection are widely studied in
cognitive neuroscience and artificial intelligence (AI), but from different
perspectives--and with different desiderata in mind (biological realism versus
scalability) that are difficult to reconcile. Here, we introduce a novel method
to plan in POMDPs--Active Inference Tree Search (AcT)--that combines the
normative character and biological realism of a leading planning theory in
neuroscience (Active Inference) and the scalability of tree search methods in
AI. This unification enhances both approaches. On the one hand, tree searches
enable the biologically grounded, first principle method of active inference to
be applied to large-scale problems. On the other hand, active inference
provides a principled solution to the exploration-exploitation dilemma, which
is often addressed heuristically in tree search methods. Our simulations show
that AcT successfully navigates binary trees that are challenging for
sampling-based methods, problems that require adaptive exploration, and the
large POMDP problem 'RockSample'--in which AcT reproduces state-of-the-art
POMDP solutions. Furthermore, we illustrate how AcT can be used to simulate
neurophysiological responses (e.g., in the hippocampus and prefrontal cortex)
of humans and other animals that solve large planning problems. These numerical
analyses show that Active Tree Search is a principled realisation of
neuroscientific and AI planning theories, which offer both biological realism
and scalability.
