An Approach to the Implementation of Overlapping Rules in Standard ML

TL;DR

This paper presents a method for implementing overlapping rules in Standard ML, enabling more flexible rule-based reactive systems, demonstrated through an implementation of Loyall's Active Behavior Trees for goal-directed agents.

Contribution

It introduces a novel approach to handle overlapping rules in Standard ML, with a practical implementation for reactive systems and agent control.

Findings

Successful implementation of overlapping rules in Standard ML

Application to goal-directed agent control in Oz environment

Framework supports reactive rule-based systems

Abstract

We describe an approach to programming rule-based systems in Standard ML, with a focus on so-called overlapping rules, that is rules that can still be active when other rules are fired. Such rules are useful when implementing rule-based reactive systems, and to that effect we show a simple implementation of Loyall's Active Behavior Trees, used to control goal-directed agents in the Oz virtual environment. We discuss an implementation of our framework using a reactive library geared towards implementing those kind of systems.