Probabilistic variants of depth-k storage automata

Develop and analyze probabilistic variants of nondeterministic depth-k storage automata (k-sna’s), and characterize their computational power and structural properties, possibly including auxiliary versions under polynomial time and logarithmic work space bounds.

Background

Prior work has explored probabilistic limited automata (a different but related memory-restricted model). The present paper suggests extending such probabilistic analysis to storage automata with depth-bounded rewriting.

This open question calls for formulating probabilistic k-sna’s and investigating their distinct capabilities and limitations.