TY - JOUR
T1 - MEvo
T2 - A framework for effective macro sets evolution
AU - Vallati, Mauro
AU - Chrpa, Lukáš
AU - Serina, Ivan
PY - 2020/7/3
Y1 - 2020/7/3
N2 - In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide “general improvement”, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros –taken from a given pool– while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.
AB - In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide “general improvement”, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros –taken from a given pool– while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.
KW - Automated Planning
KW - Domain Model Reformation
KW - Sets Evolution
UR - http://www.scopus.com/inward/record.url?scp=85073948172&partnerID=8YFLogxK
U2 - 10.1080/0952813X.2019.1672796
DO - 10.1080/0952813X.2019.1672796
M3 - Article
VL - 32
SP - 685
EP - 703
JO - Journal of Experimental and Theoretical Artificial Intelligence
JF - Journal of Experimental and Theoretical Artificial Intelligence
SN - 0952-813X
IS - 4
ER -