Reformulation Techniques for Automated Planning: A Systematic Review

TL;DR

This paper systematically reviews reformulation techniques in classical automated planning, comparing their strengths and weaknesses to guide future research in improving plan generation efficiency.

Contribution

It provides a comprehensive overview and qualitative comparison of existing reformulation techniques, highlighting their advantages and limitations.

Findings

Classifies reformulation techniques into distinct categories.

Identifies key strengths and weaknesses of each technique.

Offers insights to guide future research in planning reformulation.

Abstract

Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.