  We treat the problem of risk-aware control for stochastic shortest path (SSP)
on Markov decision processes (MDP). Typically, expectation is considered for
SSP, which however is oblivious to the incurred risk. We present an alternative
view, instead optimizing conditional value-at-risk (CVaR), an established risk
measure. We treat both Markov chains as well as MDP and introduce, through
novel insights, two algorithms, based on linear programming and value
iteration, respectively. Both algorithms offer precise and provably correct
solutions. Evaluation of our prototype implementation shows that risk-aware
control is feasible on several moderately sized models.
