Augmenting Markov Decision Processes with Advising

Abstract

This paper introduces Advice-MDPs, an expansion of Markov Decision Processes for generating policies that take into consideration advising on the desirability, undesirability, and prohibition of certain states and actions. AdviceMDPs enable the design of designing semi-autonomous systems (systems that require operator support for at least handling certain situations) that can efficiently handle unexpected complex environments. Operators, through advising, can augment the planning model for covering unexpected real-world irregularities. This advising can swiftly augment the degree of autonomy of the system, so it can work without subsequent human intervention. This paper details the Advice-MDP formalism, a fast AdviceMDP resolution algorithm, and its applicability for real-world tasks, via the design of a professional-class semi-autonomous robot system ready to be deployed in a wide range of unexpected environments and capable of efficiently integrating operator advising.