|
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. |