Ai-Chang , M., Bresina , J., Charest , L., Chase , A., Hsu , J.-J., Jonsson , A., Kanefsky , B., Morris , P., Rajan , K., Yglesias , J., et al. 2004. Mapgen: mixed-initiative planning and scheduling for the mars exploration rover mission. IEEE Intelligent Systems 19(1), 8–12.

Alhossaini , M. & Beck , J. C. 2013. Instance-specific remodelling of planning domains by adding macros and removing operators. In Tenth Symposium of Abstraction, Reformulation, and Approximation.

Amarel , S. 1968. On representations of problems of reasoning about actions. Machine Intelligence.

Areces , C. E., Bustos , F., Dominguez , M. & Hoffmann , J. 2014. Optimizing planning domains by automatic action schema splitting. In Twenty-Fourth International Conference on Automated Planning and Scheduling.

Armano , G., Cherchi , G. & Vargiu , E. 2004. Automatic generation of macro-operators from static domain analysis. In Proceedings of the 16th European Conference on Artificial Intelligence, 955–956.

Asai , M. & Fukunaga , A. 2015. Solving large-scale planning problems by decomposition and macro generation. In Proceedings of the International Conference on Automated Planning and Scheduling, 25, 16–24.

Baryannis , G., Kritikos , K. & Plexousakis , D. 2017. A specification-based QoS-aware design framework for service-based applications. Service Oriented Computing and Applications 11(3), 301–314. ISSN 1863-2394. doi: 10.1007/s11761-017-0210-4.

Baryannis , G. & Plexousakis , D. 2013. WSSL: a fluent calculus-based language for web service specifications. In 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Salinesi , C., Norrie , M. C. & Pastor , Ó. (eds), Lecture Notes in Computer Science 7908, 256–271. Springer Berlin Heidelberg. ISBN 978-3-642-38708-1. doi: 10.1007/978-3-642-38709-8_17.

Baryannis , G. & Plexousakis , D. 2014. Fluent calculus-based semantic web service composition and verification using WSSL. In 9th International Workshop on Semantic Web Enabled Software Engineering (SWESE2013), co-located with ICSOC 2013, Lomuscio, A., et al. (eds), Lecture Notes in Computer Science 8377, 256–270. Springer International Publishing Switzerland. doi: 10.1007/978-3-319-06859-6_23.

Baryannis , G., Validi , S., Dani , S. & Antoniou , G. 2019. Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research 57(7), 2179–2202. doi: 10.1080/00207543.2018.1530476.

Bocchese , A. F., Fawcett , C., Vallati , M., Gerevini , A. E. & Hoos , H. H. 2018. Performance robustness of AI planners in the 2014 international planning competition. AI Community 31(6), 445–463.

Botea , A., Enzenberger , M., Müller , M. & Schaeffer , J. 2005. Macro-ff: improving ai planning with automatically learned macro-operators. Journal of Artificial Intelligence Research 24, 581–621.

Cardellini , M., Maratea , M., Vallati , M., Boleto , G. & Oneto , L. 2021. In-station train dispatching: a PDDL+ planning approach. In Proceedings of the International Conference on Automated Planning and Scheduling, 450–458.

Castellanos-Paez , S., Rombourg , R. & Lalanda , P. 2021a. ERA: Extracting planning macro-operators from adjacent and non-adjacent sequences. In Pacific Rim Knowledge Acquisition Workshop, 30–45. Springer.

Castellanos-Paez , S., Rombourg , R. & Lalanda , P. 2021b. On the relevance of extracting macro-operators with non-adjacent actions: does it matter? In 13th International Conference on Agents and Artificial Intelligence.

Chrpa , L. 2010a. Combining learning techniques for classical planning: Macro-operators and entanglements. In 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2, 79–86. IEEE.

Chrpa , L. 2010b. Generation of macro-operators via investigation of action dependencies in plans. The Knowledge Engineering Review 25(3), 281–297.

Chrpa , L. and Barták , R. 2009. Reformulating planning problems by eliminating unpromising actions. In Eighth Symposium on Abstraction, Reformulation, and Approximation.

Chrpa , L. and McCluskey , T. L. 2012. On exploiting structures of classical planning problems: Generalizing entanglements. In ECAI, 240–245.

Chrpa , L., McCluskey , T. L. & Osborne , H. 2012. Reformulating planning problems: a theoretical point of view. In 25th International Florida Artificial Intelligence Research Society Conference, 14–19.

Chrpa , L., Scala , E. & Vallati , M. 2015a. Towards a reformulation based approach for efficient numeric planning: numeric outer entanglements. In Eighth Annual Symposium on Combinatorial Search.

Chrpa , L. and Siddiqui , F. H. 2015. Exploiting block deordering for improving planners efficiency. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

Chrpa , L. and Vallati , M. 2019. Improving domain-independent planning via critical section macro-operators. In Proceedings of the AAAI Conference on Artificial Intelligence, 7546–7553.

Chrpa , L. and Vallati , M. 2022. Planning with critical section macros: theory and practice. Journal of Artificial Intelligence Research.

Chrpa , L., Vallati , M., McCluskey , T. L. and Kitchin , D. 2013. Generating macro-operators by exploiting inner entanglements. In Tenth Symposium of Abstraction, Reformulation, and Approximation.

Chrpa , L., Vallati , M. & McCluskey , T. L. 2014. Mum: a technique for maximising the utility of macro-operators by constrained generation and use. In Twenty-Fourth International Conference on Automated Planning and Scheduling.

Chrpa , L., Vallati , M. & McCluskey , T. L. 2015b. On the online generation of effective macro-operators. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

Chrpa , L., Vallati , M. & McCluskey , T. L. 2018. Outer entanglements: a general heuristic technique for improving the efficiency of planning algorithms. Journal of Experimental & Theoretical Artificial Intelligence 30(6), 831–856.

Chrpa , L., Vallati , M. & McCluskey , T. L. 2019. Inner entanglements: narrowing the search in classical planning by problem reformulation. Computational Intelligence 35(2), 395–429.

Cooper , M. C., Maris , F. & Régnier , P. 2010. Compilation of a high-level temporal planning language into PDDL 2.1. In 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 181–188.

Corrêa , A. B., Pommerening , F., Helmert , M. & Frances , G. 2020. Lifted successor generation using query optimization techniques. In Proceedings of the International Conference on Automated Planning and Scheduling, 30, 80–89.

Dawson , C. & Siklossy , L. 1977. The role of preprocessing in problem solving systems: “an ounce of reflection is worth a pound of backtracking”. In Proceedings of the 5th International Joint Conference on Artificial Intelligence-Volume 1, 465–471.

Dodaro , C., Maratea , M. & Vallati , M. 2022. On the configuration of more and less expressive logic programs. In Theory and Practice of Logic Programming, 1–29. doi: 10.1017/S1471068422000096.

Dulac , A., Pellier , D., Fiorino , H. & Janiszek , D. 2013. Learning useful macro-actions for planning with n-grams. In 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 803–810.

Fikes , R. E. & Nilsson , N. J. 1971. Strips: a new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3–4), 189–208.

Fox , M. & Long , D. 1998. The automatic inference of state invariants in TIM. Journal of Artificial Intelligence Research 9, 367–421.

Fox , M. & Long , D. 2003. PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124.

Fox , M. & Long , D. 2006. Modelling mixed discrete-continuous domains for planning. Journal of Artificial Intelligence Research 27, 235–297.

Franco , S., Vallati , M., Lindsay , A. & McCluskey , T. L. 2019. Improving planning performance in PDDL+ domains via automated predicate reformulation. In Computational Science - ICCS 491–498.

Gerevini , A. E., Haslum , P., Long , D., Saetti , A. & Dimopoulos , Y. 2009. Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artificial Intelligence 173(5–6), 619–668.

Gerevini , A., Saetti , A. & Vallati , M. 2014. Planning through automatic portfolio configuration: the pbp approach. Journal of Artificial Intelligence Research 50, 639–696.

Gerevini , A. & Schubert , L. 1998. Inferring state constraints for domain-independent planning. In Proceedings of the American Artificial Intelligence Conference, AAAI/IAAI, 905–912.

Ghallab , M., Nau , D. & Traverso , P. 2004. Automated Planning: Theory and Practice. Elsevier.

Grastien , A. & Scala , E. 2020. CPCES: a planning framework to solve conformant planning problems through a counterexample guided refinement. Artificial Intelligence 284, 103271.

Haslum , P. & Jonsson , P. 2000. Planning with reduced operator sets. In AIPS, 150–158.

Hoffmann , J. & Nebel , B. 2001. The ff planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302.

Hofmann , T., Niemueller , T. & Lakemeyer , G. 2017. Initial results on generating macro actions from a plan database for planning on autonomous mobile robots. In Twenty-Seventh International Conference on Automated Planning and Scheduling.

Hofmann , T., Niemueller , T. & Lakemeyer , G. 2020. Macro operator synthesis for adl domains. In ECAI 2020, 761–768. IOS Press.

Horcčk, R. and Fišer, D. 2021. Endomorphisms of lifted planning problems. In Proceedings of the International Conference on Automated Planning and Scheduling, 31, 174–183.

Howe , A. E. and Dahlman , E. 2002. A critical assessment of benchmark comparison in planning. Journal of Artificial Intelligence Research 17, 1–33.

Iba , G. A. 1985. Learning by discovering macros in puzzle solving. In Proceedings of the 9th International Joint Conference on Artificial Intelligence - Volume 1, 640–642.

Iba , G. A. 1989. A heuristic approach to the discovery of macro-operators. Machine Learning 3(4), 285–317.

Jiménez , S., De La Rosa , T., Fernández , S., Fernández , F. & Borrajo , D. 2012. A review of machine learning for automated planning. The Knowledge Engineering Review 27(4), 433–467.

Kitchenham , B. & Charters , S. 2017. Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01, EBSE Technical Report.

Korf , R. E. 1985. Macro-operators: a weak method for learning. Artificial Intelligence 26(1), 35–77.

Kovács , D. L. 2012. A multi-agent extension of PDDL3.1. In Proceedings of the 3rd Workshop on the International Planning Competition (IPC), ICAPS-2012, Atibaia, Sao Paulo, Brazil, 19–27.

Long , D., Fox , M. & Hamdi , M. 2002. Reformulation in planning. In International Symposium on Abstraction, Reformulation, and Approximation, 18–32.

McCluskey , T. L. 1987. Combining weak learning heuristics in general problem solvers. In Proceedings of the 10th International Joint Conference on Artificial Intelligence - Volume 1, IJCAI’87, San Francisco, CA, USA, 331–333. Morgan Kaufmann Publishers Inc.

McCluskey , T. L. & Vallati , M. 2017. Embedding automated planning within urban traffic management operations. In Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, ICAPS, 391–399. AAAI Press.

McDermott , D. M. 2000. The 1998 AI planning systems competition. AI Magazine 21(2), 35–35.

Minton , S. 1985. Selectively generalizing plans for problem-solving. In IJCAI, 596–599. Citeseer.

Newton , M. H. & Levine , J. 2007. Wizard: suggesting macro-actions comprehensively. In Proceedings of the Doctoral Consortium held at ICAPS, 7.

Palacios , H. & Geffner , H. 2009. Compiling uncertainty away in conformant planning problems with bounded width. Journal of Artificial Intelligence Research 35, 623–675.

Pednault , E. P. 1987. Formulating multiagent, dynamic-world problems in the classical planning framework. In Reasoning About Actions & Plans, 47–82. Elsevier.

Percassi , F. & Gerevini , A. E. 2019. On compiling away PDDL3 soft trajectory constraints without using automata. In Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, ICAPS, 320–328.

Percassi , F., Scala , E. & Vallati , M. 2021. Translations from discretised PDDL+ to numeric planning. In Proceedings of the International Conference on Automated Planning and Scheduling, 31, 252–261.

Piacentini , C., Alimisis , V., Fox , M. & Long , D. 2013. Combining a temporal planner with an external solver for the power balancing problem in an electricity network. In Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, ICAPS. AAAI.

Ramrez , M., Papasimeon , M., Lipovetzky , N., Benke , L., Miller , T., Pearce , A. R., Scala , E. & Zamani , M. 2018. Integrated hybrid planning and programmed control for real time UAV maneuvering. In Proceedings of AAMAS, 1318–1326.

Riddle , P., Barley , M. & Franco , S. 2013. Problem reformulation as meta-level search. In Proceedings of the Conference on Advances in Cognitive Systems.

Riddle , P., Barley , M., Franco , S. & Douglas , J. 2015a. Automated transformation of PDDL representations. In International Symposium on Combinatorial Search.

Riddle , P., Barley , M., Franco , S. & Douglas , J. 2015b. Bagged representations in PDDL. In 4th Workshop on the International Planning Competition, 17–23.

Riddle , P., Barley , M., Franco , S. & Douglas , J. 2015c. Analysis of bagged representations in PDDL. In Workshop on Heuristics and Search for Domain-independent Planning, 17–23.

Riddle , P., Douglas , J., Barley , M. & Franco , S. 2016. Improving performance by reformulating PDDL into a bagged representation. In Proceedings of the 8th Workshop on Heuristic Search for Domain-independent Planning (HSDIP@ ICAPS), 28–36.

Riddle , P. J., Holte , R. C. & Barley , M. W. 2011. Does representation matter in the planning competition? In Ninth Symposium of Abstraction, Reformulation, and Approximation.

Sanner , S. 2010. Relational dynamic influence diagram language (rddl): language description. Unpublished ms. Australian National University 32, 27.

Scala , E. 2014. Plan repair for resource constrained tasks via numeric macro actions. In Proceedings of the Twenty-Fourth International Conferenc on International Conference on Automated Planning and Scheduling, ICAPS’14, 280–288. AAAI Press. ISBN 9781577356608.

Serina , I. 2010. Kernel functions for case-based planning. Artificial Intelligence 174(16–17), 1369–1406.

Siddiqui , F. H. & Haslum , P. 2012. Block-structured plan deordering. In Australasian Joint Conference on Artificial Intelligence, 803–814.

Taig , R. & Brafman , R. I. 2013. Compiling conformant probabilistic planning problems into classical planning. In Twenty-Third International Conference on Automated Planning and Scheduling.

Vallati , M., Chrpa , L. & Kitchin , D. 2013. An automatic algorithm selection approach for planning. In 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 1–8. IEEE.

Vallati , M., Chrpa , L. & McCluskey , T. 2017. Improving a planner’s performance through online heuristic configuration of domain models. In Proceedings of the 10th International Symposium on Combinatorial Search (SoCS 2017), 171–172.

Vallati , M., Chrpa , L., McCluskey , T. L. & Hutter , F. 2021. On the importance of domain model configuration for automated planning engines. Journal of Automated Reasoning 65(6), 727–773.

Vallati , M., Chrpa , L. & Serina , I. 2020. Mevo: a framework for effective macro sets evolution. Journal of Experimental & Theoretical Artificial Intelligence 32(4), 685–703.

Vallati , M., Hutter , F., Chrpa , L. & McCluskey , T. L. 2015. On the effective configuration of planning domain models. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

Vallati , M. & Kitchin , D. 2020. Knowledge Engineering Tools and Techniques for AI Planning. Springer.

Vallati , M. & Serina , I. 2018. A general approach for configuring PDDL problem models. In Twenty-Eighth International Conference on Automated Planning and Scheduling.

Younes , H. L. & Littman , M. L. 2004. PPDDL1.0: an extension to PDDL for expressing planning domains with probabilistic effects. Technical Report CMU-CS-04-162, 2, 99.