  A set of variables is the Markov blanket of a random variable if it contains
all the information needed for predicting the variable. If the blanket cannot
be reduced without losing useful information, it is called a Markov boundary.
Identifying the Markov boundary of a random variable is advantageous because
all variables outside the boundary are superfluous. Hence, the Markov boundary
provides an optimal feature set. However, learning the Markov boundary from
data is challenging for two reasons. If one or more variables are removed from
the Markov boundary, variables outside the boundary may start providing
information. Conversely, variables within the boundary may stop providing
information. The true role of each candidate variable is only manifesting when
the Markov boundary has been identified. In this paper, we propose a new
Tsetlin Machine (TM) feedback scheme that supplements Type I and Type II
feedback. The scheme introduces a novel Finite State Automaton - a
Context-Specific Independence Automaton. The automaton learns which features
are outside the Markov boundary of the target, allowing them to be pruned from
the TM during learning. We investigate the new scheme empirically, showing how
it is capable of exploiting context-specific independence to find Markov
boundaries. Further, we provide a theoretical analysis of convergence. Our
approach thus connects the field of Bayesian networks (BN) with TMs,
potentially opening up for synergies when it comes to inference and learning,
including TM-produced Bayesian knowledge bases and TM-based Bayesian inference.
