Decision-theoretic selection of reasoning scheme for an autonomous robot under resource constraints

Abstract

Investigates a formally rigorous technique based on decision-theoretic principles that address the problem of bounding the amount of reasoning cost under resource constraints and uncertainty. The authors derive a set of optimal decision policies that may be used to decide which reasoning scheme to use and where it should be used. The decision model, which is represented by an influence diagram, takes into account all sources of uncertainty and risk preferences in order to derive the optimal decision policy by deliberating on the trade-off between the value of the information that is potentially available and the computational cost. By developing such a metalevel decision model, the authors construct an intelligent agent that is embedded in the environment and can behave in a sophisticated fashion with minimal environmental interactions. The authors apply such an agent architecture to an autonomous mobile robot with limited sensing and actuating capabilities, and present the simulated behaviors which are supervised by their proposed model.