  In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs)
to learn complex state transition models of planning domains with discretized
factored state and action spaces. In order to directly exploit this transition
structure for planning, we present two novel compilations of the learned
factored planning problem with BNNs based on reductions to Weighted Partial
Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear
Programming (FD-BLP-Plan+). Theoretically, we show that our SAT-based
Bi-Directional Neuron Activation Encoding is asymptotically the most compact
encoding relative to the current literature and supports Unit Propagation (UP)
-- an important property that facilitates efficiency in SAT solvers.
Experimentally, we validate the computational efficiency of our Bi-Directional
Neuron Activation Encoding in comparison to an existing neuron activation
encoding and demonstrate the ability to learn complex transition models with
BNNs. We test the runtime efficiency of both FD-SAT-Plan+ and FD-BLP-Plan+ on
the learned factored planning problem showing that FD-SAT-Plan+ scales better
with increasing BNN size and complexity. Finally, we present a finite-time
incremental constraint generation algorithm based on generalized landmark
constraints to improve the planning accuracy of our encodings through simulated
or real-world interaction.
