Supervisory Control for Behavior Composition

TL;DR

This paper establishes a connection between behavior composition in AI and supervisory control theory in discrete event systems, enabling the use of control tools to synthesize behavior modules with preferences.

Contribution

It introduces a novel framework linking AI behavior composition to supervisory control, allowing for leveraging existing control theory tools and incorporating preferences.

Findings

The framework simplifies the implementation of behavior modules.

Preferences can be integrated easily into the control synthesis.

The approach benefits from established discrete event systems theory.

Abstract

We relate behavior composition, a synthesis task studied in AI, to supervisory control theory from the discrete event systems field. In particular, we show that realizing (i.e., implementing) a target behavior module (e.g., a house surveillance system) by suitably coordinating a collection of available behaviors (e.g., automatic blinds, doors, lights, cameras, etc.) amounts to imposing a supervisor onto a special discrete event system. Such a link allows us to leverage on the solid foundations and extensive work on discrete event systems, including borrowing tools and ideas from that field. As evidence of that we show how simple it is to introduce preferences in the mapped framework.