Spatiotemporal Abstraction of Stochastic Sequential Processes
Probabilistic finite state machines have become a popular modelingto ol for representing sequential processes, ranging from images and speech signals to text documents and spatial and genomic maps. In this paper, I describe two hierarchical abstraction mechanisms for simplifyingthe (estimation) learningand (control) optimization of complex Markov processes: spatial decomposition and temporal aggregation. I present several approaches to combiningspatial and temporal abstraction, drawingup on recent work of my group as well as that of others. I show how spatiotemporal abstraction enables improved solutions to three difficult sequential estimation and decision problems: hidden state modelingand control, learningparallel plans, and coordinatingwith multiple agents.