Stochastic self-monitoring of autonomous systems

Abstract

A probabilistic method is presented for high level task planning of autonomous, mobile systems under partial observability of states and partial knowledge of transition laws. Partial state observability is addressed by directed Markov fields which support the detection of relationships between pairs of observed variables in competition to other such pairs.