  Risk averse decision making under uncertainty in partially observable domains
is a fundamental problem in AI and essential for reliable autonomous agents. In
our case, the problem is modeled using partially observable Markov decision
processes (POMDPs), when the value function is the conditional value at risk
(CVaR) of the return. Calculating an optimal solution for POMDPs is
computationally intractable in general. In this work we develop a
simplification framework to speedup the evaluation of the value function, while
providing performance guarantees. We consider as simplification a
computationally cheaper belief-MDP transition model, that can correspond, e.g.,
to cheaper observation or transition models. Our contributions include general
bounds for CVaR that allow bounding the CVaR of a random variable X, using a
random variable Y, by assuming bounds between their cumulative distributions.
We then derive bounds for the CVaR value function in a POMDP setting, and show
how to bound the value function using the computationally cheaper belief-MDP
transition model and without accessing the computationally expensive model in
real-time. Then, we provide theoretical performance guarantees for the
estimated bounds. Our results apply for a general simplification of a
belief-MDP transition model and support simplification of both the observation
and state transition models simultaneously.
