  A standard objective in partially-observable Markov decision processes
(POMDPs) is to find a policy that maximizes the expected discounted-sum payoff.
However, such policies may still permit unlikely but highly undesirable
outcomes, which is problematic especially in safety-critical applications.
Recently, there has been a surge of interest in POMDPs where the goal is to
maximize the probability to ensure that the payoff is at least a given
threshold, but these approaches do not consider any optimization beyond
satisfying this threshold constraint. In this work we go beyond both the
"expectation" and "threshold" approaches and consider a "guaranteed payoff
optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and
the objective is to find a policy $\sigma$ such that a) each possible outcome
of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected
discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all
policies satisfying a). We present a practical approach to tackle the GPO
problem and evaluate it on standard POMDP benchmarks.

Artificial Intelligence;Computer Science and Game Theory