TY - JOUR
T1 - Reformulation techniques for automated planning
T2 - a systematic review
AU - Alarnaouti, Diaeddin
AU - Baryannis, George
AU - Vallati, Mauro
N1 - Funding Information:
Mauro Vallati is supported by the UKRI Future Leaders Fellowship [grant number MR/T041196/1]. 1
Publisher Copyright:
© The Author(s), 2023. Published by Cambridge University Press.
PY - 2023/11/8
Y1 - 2023/11/8
N2 - 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, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize 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.
AB - 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, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize 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.
KW - Automated planning
KW - Artificial Intelligence
KW - Intelligent autonomous agents
UR - http://www.scopus.com/inward/record.url?scp=85177977292&partnerID=8YFLogxK
U2 - 10.1017/S0269888923000097
DO - 10.1017/S0269888923000097
M3 - Review article
VL - 38
JO - Knowledge Engineering Review
JF - Knowledge Engineering Review
SN - 0269-8889
IS - 1
M1 - e9
ER -