  Algorithmic assurances from advanced autonomous systems assist human users in
understanding, trusting, and using such systems appropriately. Designing these
systems with the capacity of assessing their own capabilities is one approach
to creating an algorithmic assurance. The idea of `machine self-confidence' is
introduced for autonomous systems. Using a factorization based framework for
self-confidence assessment, one component of self-confidence, called
`solver-quality', is discussed in the context of Markov decision processes for
autonomous systems. Markov decision processes underlie much of the theory of
reinforcement learning, and are commonly used for planning and decision making
under uncertainty in robotics and autonomous systems. A `solver quality' metric
is formally defined in the context of decision making algorithms based on
Markov decision processes. A method for assessing solver quality is then
derived, drawing inspiration from empirical hardness models. Finally, numerical
experiments for an unmanned autonomous vehicle navigation problem under
different solver, parameter, and environment conditions indicate that the
self-confidence metric exhibits the desired properties. Discussion of results,
and avenues for future investigation are included.
