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Research Article

Improving Domain-Independent Heuristic State-Space Planning via plan cost predictions

, , , &
Pages 849-875 | Received 22 Jul 2020, Accepted 26 Jun 2021, Published online: 31 Oct 2021

References

  • Areces, C., Bustos, F., Domínguez, M. A., & Hoffmann, J. (2014). Optimizing planning domains by automatic action schema splitting. In Proceedings of the twenty-fourth international conference on automated planning and scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014. AAAI Press.
  • Blum, A., & Furst, M. L. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90(1–2), 281–300. https://doi.org/10.1016/S0004-3702(96)00047-1
  • Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1–2), 5–33. https://doi.org/10.1016/S0004-3702(01)00108-4
  • Bonisoli, A., Gerevini, A., Saetti, A., & Serina, I. (2015). Effective plan retrieval in case-based planning for metric-temporal problems. Journal of Experimental and Theoretical Artificial Intelligence, 27(5), 603–647. https://doi.org/10.1080/0952813X.2014.993506
  • Borrajo, D., Roubícková, A., & Serina, I. (2014). Progress in case-based planning. ACM Computing Surveys, 47(2), 35:1–35: 39. https://doi.org/10.1145/2674024
  • Botea, A., Enzenberger, M., Müller, M., & Schaeffer, J. (2005). Macro-FF: Improving AI planning with automatically learned macro-operators. J. Artif.Intell. Res. (JAIR), 24, 581–621. https://doi.org/10.1613/jair.1696
  • Celorrio, S. J., De La Rosa, T., Fernández, S., Fernández, F., & Borrajo, D. (2012). A review of machine learning for automated planning. The Knowledge Engineering Review (KER), 27(4), 433–467. https://doi.org/10.1017/S026988891200001X
  • Cenamor, I., De La Rosa, T., & Fernández, F. (2012). Mining IPC-2011 results. In Proceedings of the 3rd workshop on the international planning competition.
  • Cenamor, I., De La Rosa, T., & Fernández, F. (2013). Learning predictive models to configure planning portfolios. In Proceedings of the 4th workshop on planning and learning (ICAPS-PAL 2013), Rome, Italy, 2013,(pp. 14–22).
  • Cenamor, I., De La Rosa, T., & Fernández, F. (2016). The ibacop planning system: Instance-based configured portfolios. The Journal of Artificial Intelligence Research, 56, 657–691. https://doi.org/10.1613/jair.5080
  • Cenamor, I., & Pozanco, A. (2019). Insights from the 2018 IPC benchmarks. In ICAPS 2019 Workshop on the International Planning Competition (WIPC), (p. 8–14).
  • Cenamor, I., Vallati, M., & Chrpa, L. (2019). On the predictability of domain- independent temporal planners. Computational Intelligence, 35(4), 745–773. https://doi.org/10.1111/coin.12211
  • Cerutti, F., Thimm, M., & Vallati, M. (2020). An experimental analysis on the similarity of argumentation semantics. Argument Comput, 11(3), 269–304. https://doi.org/10.3233/AAC-200907
  • Chrpa, L., & Vallati, M. (2019). Improving domain-independent planning via critical section macro-operators. In The thirty-third AAAI conference on artificial intelligence, Honolulu, Hawaii, USA, January 27 - February 1, 2019 (pp. 7546–7553). AAAI Press.
  • 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. https://doi.org/10.1111/coin.12203
  • De La Rosa, T., Celorrio, S. J., Fuentetaja, R., & Borrajo, D. (2011). Scaling up heuristic planning with relational decision trees. J. Artif.Intell. Res. (JAIR), 40(40), 767–813. https://doi.org/10.1613/jair.3231
  • Domshlak, C., Hoffmann, J., & Katz, M. (2015). Red-black planning: A new systematic approach to partial delete relaxation. Artificial Intelligence, 221, 73–114. https://doi.org/10.1016/j.artint.2014.12.008
  • Fawcett, C., Vallati, M., Hutter, F., Hoffmann, J., Hoos, H. H., & Leyton-Brown, K. (2014). Improved features for runtime prediction of domain-independent planners. In Proceedings of the twenty-fourth international conference on automated planning and scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014. AAAI Press.
  • Fikes, R., & Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3/4), 189–208. https://doi.org/10.1016/0004-3702(71)90010-5
  • Fox, M., & Long, D. (2003). PDDL2.1: An extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR), 20, 61–124. https://doi.org/10.1613/jair.1129
  • Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. T., & Ziller, S. (2011). A portfolio solver for answer set programming: Preliminary report. In Proceedings of the 11th international conference logic programming and nonmonotonic reasoning (LPNMR), Vancouver, Canada, May 16-19, 2011,(pp. 352–357). Springer.
  • Gebser, M., Kaufmann, B., Neumann, A., & Schaub, T. (2007). clasp: A conflict- driven answer set solver. In Proceedings of the 9th international conference logic programming and nonmonotonic reasoning (LPNMR), Tempe, AZ, USA, May 15-17, 2007, (pp. 260–265). Springer.
  • Gerevini, A., 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. https://doi.org/10.1016/j.artint.2008.10.012
  • Gerevini, A., Lipovetzky, N., Peli, N., Percassi, F., Saetti, A., & Serina, I. (2019). Novelty messages filtering for multi agent privacy-preserving planning. In Proceedings of the twelfth international symposium on combinatorial search, SOCS, Napa, California, 16-17 July 2019, (pp. 79–87). AAAI Press.
  • Gerevini, A., Lipovetzky, N., Percassi, F., Saetti, A., & Serina, I. (2019). Best- first width search for multi agent privacy-preserving planning. In Proceedings of the twenty-ninth international conference on automated planning and scheduling, ICAPS, Berkeley, CA, USA, July 11-15, 2019, (pp. 163–171). AAAI Press.
  • Gerevini, A., Saetti, A., & Serina, I. (2003). Planning through stochastic local search and temporal action graphs. J. Artif.Intell. Res. (JAIR), 20, 239–290. https://doi.org/10.1613/jair.1183
  • Gerevini, A., Saetti, A., & Serina, I. (2006). An approach to temporal planning and scheduling in domains with predictable exogenous events. The Journal of Artificial Intelligence Research, 25, 187–231. https://doi.org/10.1613/jair.1742
  • Gerevini, A., Saetti, A., & Serina, I. (2008). An approach to efficient planning with numerical fluents and multi-criteria plan quality. Artificial Intelligence, 172(8–9), 899–944. https://doi.org/10.1016/j.artint.2008.01.002
  • Gerevini, A., Saetti, A., & Serina, I. (2011a). An empirical analysis of some heuristic features for planning through local search and action graphs. Fundam. Inform, 107(2–3), 167–197. https://doi.org/10.3233/FI-2011-399
  • Gerevini, A., Saetti, A., & Serina, I. (2011b). Planning in domains with derived predicates through rule-action graphs and local search. Annals of Mathematics and Artificial Intelligence, 62(3–4), 259–298. https://doi.org/10.1007/s10472-011-9240-3
  • Gerevini, A., Saetti, A., & Vallati, M. (2014). Planning through automatic portfolio configuration: The pbp approach. J. Artif.Intell. Res. (JAIR), 50, 639–696. https://doi.org/10.1613/jair.4359
  • Gerevini, A., Saetti, A., & Vallati, M. (2015). Exploiting macro-actions and predicting plan length in planning as satisfiability. AI Communications, 28(2), 323–344. https://doi.org/10.3233/AIC-140641
  • Gerevini, A., & Schubert, L. K. (2000). Discovering state constraints in DISCOPLAN: Some new results. In Proceedings of the seventeenth national conference on artificial intelligence and twelfth conference on on innovative applications of artificial intelligence, Austin, Texas, USA, July 30 - August 3, 2000, (pp. 761–767). AAAI Press / The MIT Press.
  • Ghallab, M., Nau, D. S., & Traverso, P. (2004). Automated planning - theory and practice. Elsevier.
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The weka data mining software: An update. ACM SIGKDD Explorations, 11(1), 10–18. https://doi.org/10.1145/1656274.1656278
  • Helmert, M. (2003). Complexity results for standard benchmark domains in planning. Artificial Intelligence, 143(2), 219–262. https://doi.org/10.1016/S0004-3702(02)00364-8
  • Helmert, M. (2006). The Fast Downward planning system. J. Artif.Intell. Res. (JAIR), 26, 191–246. https://doi.org/10.1613/jair.1705
  • Helmert, M., & Domshlak, C. (2009). Landmarks, critical paths and abstractions: What’s the difference anyway? In Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, September 19-23, 2009, AAAI Press.
  • Hoffmann, J. (2011). Analyzing search topology without running any search: On the connection between causal graphs and h+. J. Artif.Intell. Res. (JAIR), 41, 155–229. https://doi.org/10.1613/jair.3276
  • Hoffmann, J., & Nebel, B. (2001). The FF planning system: Fast plan generation through heuristic search. J. Artif.Intell. Res. (JAIR), 14, 253–302. https://doi.org/10.1613/jair.855
  • Hoos, H., Lindauer, M. T., & Schaub, T. (2014). claspfolio 2: Advances in algorithm selection for answer set programming. Theory and Practice of Logic Programming, 14(4–5), 569–585. https://doi.org/10.1017/S1471068414000210
  • Howe, A., Dahlman, E., Hansen, C., Von Mayrhauser, A., & Scheetz, M. (1999). Exploiting competitive planner performance. In Recent Advances in AI Planning, 5th European Conference on Planning, ECP'99, Durham, UK, September 8-10, 1999, Proceedings, (pp. 62–72). Springer.
  • Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stützle, T. (2009). Paramils: An automatic algorithm configuration framework. J. Artif. Intell. Res. (JAIR), 36, 267–306. https://doi.org/10.1613/jair.2861
  • Hutter, F., Xu, L., Hoos, H. H., & Leyton-Brown, K. (2014). Algorithm runtime prediction: Methods & evaluation. Artificial Intelligence, 206, 79–111. https://doi.org/10.1016/j.artint.2013.10.003
  • Korf, R. E. (1985). Macro-operators: A weak method for learning. Artif. Intell, 26(1), 35–77. https://doi.org/10.1016/0004-3702(85)90012-8 Retrieved from
  • Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F., & Leyton-Brown, K. (2017). Auto- weka 2.0: Automatic model selection and hyperparameter optimization in WEKA. Journal of Machine Learning Research, 18(25), 1–25.http://jmlr.org/papers/v18/16-261.html
  • Krajnanskỳ, M., Hoffmann, J., Buffet, O., & Fern, A. (2014). Learning pruning rules for heuristic search planning. In Proceedings of the twenty-first european conference on artificial intelligence (ECAI 2014), Prague, Czech Republic, 18-22 August 2014 (pp. 483–488). IOS Press.
  • Lelis, L. H., Stern, R., Jabbari Arfaee, S., Zilles, S., Felner, A., & Holte, R. C. (2016). Predicting optimal solution costs with bidirectional stratified sampling in regular search spaces. Artificial Intelligence, 230, 51–73. https://doi.org/10.1016/j.artint.2015.09.012
  • Lipovetzky, N., & Geffner, H. (2011). Searching for plans with carefully designed probes. In Proceedings of the 21st International Conference on Automated Planning and Scheduling, ICAPS 2011, Freiburg, Germany June 11-16, 2011, 154–161.
  • Maratea, M., Pulina, L., & Ricca, F. (2014). A multi-engine approach to answer-set programming. Theory and Practice of Logic Programming, 14(6), 841–868. https://doi.org/10.1017/S1471068413000094
  • Marquardt, W., . D., & Snee, D. (1975). Ridge regression in practice. The American Statistician, 29(1), 3–20.
  • Matos, P., Planes, J., Letombe, F., & Marques-Silva, J. (2008). A MAX-SAT algorithm portfolio. ECAI 2008 - 18th European Conference on Artificial Intelligence, Patras, Greece, July 21-25, 2008, Proceedings (pp. 911–912). IOS Press.
  • Nakhost, H., Müller, M., Valenzano, R., & Xie, F. (2011). Arvand: The art of random walks. In Booklet of the seventh international planning competition.
  • Nissim, R., & Brafman, R. I. (2014). Distributed heuristic forward search for multi- agent planning. The Journal of Artificial Intelligence Research, 51, 293–332. https://doi.org/10.1613/jair.4295
  • Percassi, F., Gerevini, A., & Geffner, H. (2017). Improving plan quality through heuristics for guiding and pruning the search: A study using LAMA. In Proceedings of the tenth international symposium on combinatorial search (SOCS 2017), Pittsburgh, Pennsylvania, June 16-17, 2017, (pp. 144–148).
  • Percassi, F., Gerevini, A. E., Scala, E., Serina, I., & Vallati, M. (2020). Generating and exploiting cost predictions in heuristic state-space planning. In Proceedings of the thirtieth international conference on automated planning and scheduling, Nancy, France, October 26-30, 2020, (pp. 569–573).
  • Pohl, I. (1970). Heuristic search viewed as path finding in a graph. Artificial Intelligence, 1(3), 193–204. https://doi.org/10.1016/0004-3702(70)90007-X
  • Pulina, L., & Tacchella, A. (2007). A multi-engine solver for quantified boolean formulas. In Principles and Practice of Constraint Programming - CP 2007, 13th International Conference, CP 2007, Providence, RI, USA, September 23-27, 2007, Proceedings, (pp. 574–589). Springer.
  • Radzi, N. H. M. (2010). Multi-objective planning using linear programming ( Unpublished doctoral dissertation). University of Strathclyde.
  • Richter, S., & Westphal, M. (2010). The LAMA planner: Guiding cost-based anytime planning with landmarks. J. Artif.Intell. Res. (JAIR), 39, 127–177. https://doi.org/10.1613/jair.2972
  • Rintanen, J. (2012). Engineering efficient planners with SAT. In ECAI 2012 - 20th European Conference on Artificial Intelligence, Montpellier, France, August 27-31, 2012, (pp. 684–689).
  • Rizzini, M., Fawcett, C., Vallati, M., Gerevini, A., & Hoos, H. (2017). Static and dynamic portfolio methods for optimal planning: An empirical analysis. International Journal on Artificial Intelligence Tools, 26(1), 1–27. https://doi.org/10.1142/S0218213017600065
  • Roberts, M., & Howe, A. E. (2009). Learning from planner performance. Artificial Intelligence, 173(5–6), 536–561. https://doi.org/10.1016/j.artint.2008.11.009
  • Roberts, M., Howe, A. E., & Ray, I. (2014). Evaluating diversity in classical planning. In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, {ICAPS} 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014, (p. 253–261).
  • Roberts, M., Howe, A. E., Wilson, B., & desJardins, M. (2008). What makes planners predictable? In Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling, ICAPS 2008, Sydney, Australia, September 14-18, 2008, (pp. 288–295).
  • Robnik-Sikonja, M., & Kononenko, I. (1997). An adaptation of relief for attribute estimation in regression. In Proceedings of the fourteenth international conference on machine learning (p. 296–304). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
  • Scala, E. (2014). Plan repair for resource constrained tasks via numeric macro actions. In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014.
  • Scala, E., & Torasso, P. (2015). Deordering and numeric macro actions for plan repair. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, 1673–1681.
  • Seipp, J., Sievers, S., Helmert, M., & Hutter, F. (2015). Automatic configuration of sequential planning portfolios. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, USA, January 25-30, 2015, (pp. 3364–3370).
  • Serina, I. (2010). Kernel functions for case-based planning. Artificial Intelligence, 174(16-17), 1369–1406. https://doi.org/10.1016/j.artint.2010.07.007
  • Shen, W., Trevizan, F. W., & Thiébaux, S. (2019).Learning domain-independent planning heuristics with hypergraph networks. In Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, Nancy, France, October 26-30, 2020,(pp. 574–584).
  • Stern, R., Felner, A., Van Den Berg, J., Puzis, R., Shah, R., & Goldberg, K. (2014). Potential-based bounded-cost search and anytime non-parametric A*. Artificial Intelligence, 214, 1–25. https://doi.org/10.1016/j.artint.2014.05.002
  • Thayer, J. T., & Ruml, W. (2011). Bounded suboptimal search: A direct approach using inadmissible estimates. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, (pp. 674–679). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-119
  • Toyer, S., Thiébaux, S., Trevizan, F. W., & Xie, L. (2020). Asnets: Deep learning for generalised planning. The Journal of Artificial Intelligence Research, 68, 1–68. https://doi.org/10.1613/jair.1.11633
  • Vallati, M., Cerutti, F., & Giacomin, M. (2019). Predictive models and abstract argumentation: The case of high-complexity semantics. The Knowledge Engineering Review, 34(34), 315–327. https://doi.org/10.1017/S0269888918000036
  • Vallati, M., Chrpa, L., & McCluskey, T. L. (2018). What you always wanted to know about the deterministic part of the international planning competition (IPC) 2014 (but were too afraid to ask). The Knowledge Engineering Review (KER), 33, e3. https://doi.org/10.1017/S0269888918000012
  • Vallati, M., Chrpa, L., & Serina, I. (2020). Mevo: A framework for effective macro sets evolution. Journal of Experimental and Theoretical Artificial Intelligence, 32(4), 685–703. https://doi.org/10.1080/0952813X.2019.1672796
  • Vallati, M., Hutter, F., Chrpa, L., & McCluskey, T. L. (2015). On the effective configuration of planning domain models. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015(pp. 1704–1711).
  • Vallati, M., & Serina, I. (2018). A general approach for configuring PDDL problem models. In Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, ICAPS 2018, Delft, The Netherlands, June 24-29, 2018, (pp. 431–436). https://aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17624
  • Vallati, M., Serina, I., Saetti, A., Gerevini, A. (2016). Identifying and exploiting features for effective plan retrieval in case-based planning. Fundam. Inform, 149(1–2), 209–240. https://doi.org/10.3233/FI-2016-1447
  • Wilcoxon, F., & Wilcox, R. A. (1964). Some rapid approximate statistical procedures. American Cyanamid Co.
  • Xu, L., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2008). SATzilla: Portfolio-based Algorithm Selection for SAT. Journal of Artificial Intelligence Research, 32, 565–606. https://doi.org/10.1613/jair.2490
  • Yoon, S. W., Fern, A., & Givan, R. (2006). Learning heuristic functions from relaxed plans. In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling, ICAPS 2006, Cumbria, UK, June 6-10, 2006 (pp. 162–171). AAAI Press.

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