Every Action Based Sensor

TL;DR

This paper extends Erdmann's theory of action-based sensors by identifying its limitations, providing a generalized method to produce sensors for all plans, and establishing a complete characterization of such sensors in planning problems.

Contribution

The paper introduces a generalized approach to derive action-based sensors, overcoming previous limitations and providing a complete characterization for planning problems.

Findings

Existing methods overlook some sensors for certain plans.

New methods can produce sensors even when previous approaches fail.

The approach offers a complete characterization of sensors in planning.

Abstract

In studying robots and planning problems, a basic question is what is the minimal information a robot must obtain to guarantee task completion. Erdmann's theory of action-based sensors is a classical approach to characterizing fundamental information requirements. That approach uses a plan to derive a type of virtual sensor which prescribes actions that make progress toward a goal. We show that the established theory is incomplete: the previous method for obtaining such sensors, using backchained plans, overlooks some sensors. Furthermore, there are plans, that are guaranteed to achieve goals, where the existing methods are unable to provide any action-based sensor. We identify the underlying feature common to all such plans. Then, we show how to produce action-based sensors even for plans where the existing treatment is inadequate, although for these cases they have no single canonical sensor. Consequently, the approach is generalized to produce sets of sensors. Finally, we show also that this is a complete characterization of action-based sensors for planning problems and discuss how an action-based sensor translates into the traditional conception of a sensor.