Large-Scale Planning Under Uncertainty: A Survey

Our research area is planning under uncertainty, that is, making sequences of decisions in the face of imperfect information. We are particularly concerned with developing planning algorithms that perform well in large, real-world domains. This paper is a brief introduction to this area of research, which draws upon results from operations research (Markov decision processes), machine learning (reinforcement learning), and artiicial intelligence (planning). Although techniques for planning under uncertainty are extremely promising for tackling real-world problems, there is a real need at this stage to look at large-scale applications to provide direction to future development and analysis.