Decision processes with incomplete state feedback have been traditionally modelled as partially observable Markov decision processes. In this article, we present an alternative formulation based on probabilistic regular languages. The proposed approach generalises the recently reported work on language measure theoretic optimal control for perfectly observable situations and shows that such a framework is far more computationally tractable to the classical alternative. In particular, we show that the infinite horizon decision problem under partial observation, modelled in the proposed framework, is lambda-approximable and, in general, is not harder to solve compared to the fully observable case. The approach is illustrated via two simple examples.