Programming Finite-Domain Constraint Propagators in Action Rules

TL;DR

This paper introduces AR, a new language for implementing finite-domain constraint propagators, combining goal-oriented and event-driven models, enabling flexible and efficient constraint programming.

Contribution

The paper presents AR, a novel language that enhances the implementation of constraint propagators with greater descriptive power and flexibility than existing indexical-based systems.

Findings

AR can implement various consistency algorithms including interval and arc-consistency.

Extended B-Prolog with AR outperforms other CLP(FD) systems in benchmarks.

Demonstrated effective implementation of constraints like all_distinct and linear equality.

Abstract

In this paper, we propose a new language, called AR ({\it Action Rules}), and describe how various propagators for finite-domain constraints can be implemented in it. An action rule specifies a pattern for agents, an action that the agents can carry out, and an event pattern for events that can activate the agents. AR combines the goal-oriented execution model of logic programming with the event-driven execution model. This hybrid execution model facilitates programming constraint propagators. A propagator for a constraint is an agent that maintains the consistency of the constraint and is activated by the updates of the domain variables in the constraint. AR has a much stronger descriptive power than {\it indexicals}, the language widely used in the current finite-domain constraint systems, and is flexible for implementing not only interval-consistency but also arc-consistency algorithms. As examples, we present a weak arc-consistency propagator for the {\tt all\_distinct} constraint and a hybrid algorithm for n-ary linear equality constraints. B-Prolog has been extended to accommodate action rules. Benchmarking shows that B-Prolog as a CLP(FD) system significantly outperforms other CLP(FD) systems.