Using Tabled Logic Programming to Solve the Petrobras Planning Problem

TL;DR

This paper presents a Picat-based planner utilizing tabling to efficiently solve the Petrobras cargo transport planning problem, outperforming Monte Carlo Tree Search in runtime and plan quality.

Contribution

Introducing a novel Picat language-based planner that leverages tabling for improved efficiency and plan quality in complex planning problems.

Findings

Tabling significantly improves runtime efficiency.

The Picat planner produces higher quality plans.

Performance surpasses Monte Carlo Tree Search.

Abstract

Tabling has been used for some time to improve efficiency of Prolog programs by memorizing answered queries. The same idea can be naturally used to memorize visited states during search for planning. In this paper we present a planner developed in the Picat language to solve the Petrobras planning problem. Picat is a novel Prolog-like language that provides pattern matching, deterministic and non-deterministic rules, and tabling as its core modelling and solving features. We demonstrate these capabilities using the Petrobras problem, where the goal is to plan transport of cargo items from ports to platforms using vessels with limited capacity. Monte Carlo Tree Search has been so far the best technique to tackle this problem and we will show that by using tabling we can achieve much better runtime efficiency and better plan quality.