PDDL4J: a planning domain description library for java

References

• Ambite, J. L., & Kapoor, D. (2007). Automatically composing data workflows with relational descriptions and shim services. In Proceedings of the International Semantic Web Conference (pp. 15–29), Busan, Korea.
   Google Scholar
• Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., & Marchetti-Spaccamela, A. (1999). Complexity and approximation. New-York, NY: Springer-Verlag.
   Google Scholar
• Backes, P. G., Norris, J. S., Powell, M. W., & Vona, M. A. (2004). Multi-mission activity planning for Mars lander and rover missions. In Proceedings of the IEEE Aerospace Conference (pp. 877–886), Big Sky, MT.
   Google Scholar
• Bäckström, C., & Nebel, B. (1995). Complexity results for SAS+ planning. Computational Intelligence, 11, 625–656.
   Web of Science ®Google Scholar
• Barták, R., Salido, M., & Rossi, F. (2010). Constraint satisfaction techniques in planning and scheduling. Journal Intelligent Manufacturing, 21(1), 5–15.
   Web of Science ®Google Scholar
• Barták, R., & Toropila, D. (2008). Reformulating constraint models for classical planning. In Proceedings of The International Florida Artificial Intelligence Research Society Conference (pp. 525–530), Coconut Gove, FL.
   Google Scholar
• Bernardini, S., & Smith, D. (2007). Developing Domain-independent search control for EUROPA2. Proceedings if the ICAPS workshop on Heuristiics for Domaine-independent planning, Providence, RI.
   Google Scholar
• Bevilacqua, L., Furno, A., Scotto di Carlo, V., & Zimeo, E. (2011). A tool for automatic generation of WS-BPEL compositions from OWL-S described services. Proceedings of the International Conference on Software, Knowledge Information, Industrial Management and Applications, Benevento, Italy.
   Google Scholar
• Blum, A., & Furst, M. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90(1–2), 281–300.
   Web of Science ®Google Scholar
• Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1--2), 5–33.
   Web of Science ®Google Scholar
• Brafman, R., & Domshlak, C. (2003). Structure and complexity in planning with unary operators. Journal of Artificial Intelligence Research, 18(1), 315–349.
   Google Scholar
• Brafman, R., & Domshlak, C. (2006). Factored planning: How, when, and when not. In Proceedings of the Association for the Advancement of Artificial Intelligence (pp. 809–814), Acapulco, Mexico.
   Google Scholar
• Brenner, M. (2003). A multiagent planning language. Proceedings of the ICAPS Workshop on Planning Domain Description Language, Trento, Italy.
   Google Scholar
• Bresina, J. L., Jónsson, A. K., Morris, P. H., & Rajan, K. (2005). Activity planning for the Mars exploration Rovers. In Proceedings of the International Conference on Automated Planning and Scheduling (pp. 40–49), Monterey, CA.
   Google Scholar
• Bryce, D., & Kambhampati, S. (2007). A tutorial on planning graph based reachability heuristics. Artificial Intelligence Magazine, 27(1), 47–83.
   Google Scholar
• Butler, R., & Muñoz, C. (2006). An abstract plan preparation language (Technical Report TM-2006-214518). NASA.
   Google Scholar
• Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., De Carolis, V., ... Maurelli, F.(2015). Dynamically extending planning models using an ontology. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 79–85), Jerusalem, Israel.
   Google Scholar
• Chen, H., & Giménez, O. (n.d.). Causal graphs and structurally restricted planning. Journal of Computer System and Science, 10, 273–314.
   Google Scholar
• Chen, Y., Zhao, X., & Zhang, W. (2007). Long-distance mutual exclusion for propositional planning. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1840–1845), Hyderabad, India.
   Google Scholar
• Cheng, C. H., Knoll, A., Luttenberger, M., & Buckl, C. (2011). GAVS+: An open platform for the research of algorithmic game solving. In Proceedings of the Joint European Conferences on Theory and Practice of Software (pp. 258–261), Sarrebruck, Germany.
   Google Scholar
• Cooper, M., de Roquemaurel, M., & Régnier, P. (2011). A weighted CSP approach to cost-optimal planning. AI communication, 24(1), 1–29.
   Web of Science ®Google Scholar
• Di Rocco, M., Pecora, F., & Saffiotti, A. (2013). Closed loop configuration planning with time and resources. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 36–44), Rome, Italy.
   Google Scholar
• Dimopoulos, Y., Nebel, B., & Koehler, J. (1997). Encoding planning problems in nonmonotonic logic programs. In Proceedings of the European Conference on Planning (pp. 169–181), Toulouse, France.
   Google Scholar
• Do, M., & Kambhampati, S. (2001). Planning as constraint satisfaction: Solving the planning graph by compiling it into CSP. Artificial Intelligence, 132(2), 151–182.
   Web of Science ®Google Scholar
• Domshlak, C., Hoffmann, J., & Katz, M. (2015). Red-black planning: A new systematic approach to partial delete relaxation. Artificial Intelligence, 221, 73–114.
   Web of Science ®Google Scholar
• Dongning, R., Zhihua, J., Yunfei, J., & Kangheng, W. (2010). Learning non-deterministic action models for web services from WSBPEL programs. Journal of Computer Research and Development, 47(3), 445–454.
   Google Scholar
• Dvor̆ák, F., Bit-Monnot, A., Ingrand, F., & Ghallab, M. (2014). A flexible ANML actor and planner in robotics. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 12–19), Portsmouth, NH.
   Google Scholar
• Ededlkamp, S. (2001). Planning with pattern databases. In Proceedings of the European conference on Planning (pp. 13–24), Toledo, Spain.
   Google Scholar
• Edelkamp, S., & Hoffmann, J. (2004). PDDL2.2: The language for the classical part of the 4th international planning competition (Technical Report 195). Institut für Informatik.
   Google Scholar
• Fernández, S., Adarve, R., Pérez, M., Rybarczyk, M., & Borrajo, D. (2006). Planning for an AI based virtual agents game. Proceedings of the ICAPS Workshop on AI Planning for Synthetic Characters and Computer Games, Ambleside, UK.
   Google Scholar
• Fernandez-Gonzalez, E., Karpas, E., & Williams, B. C. (2015). Mixed discrete-continuous heuristic generative planning based on flow tubes. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 106–115), Jerusalem, Israel.
   Google Scholar
• Ferrer-Mestres, J., Francès, G., & Geffner, H. (2015). Planning with state constraints and its applications to combined task and motion planning. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 13–22), Jerusalem, Israel.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Fox, M., & Long, D. (2002). PDDL+ : Modelling continuous time-dependent effects. Proceedings of the International NASA Workshop on Planning and Scheduling, Moffett Field, USA.
   Google Scholar
• Fox, M., & Long, D. (2003). PDDL 2.1: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research, 20(1), 61–124.
   Google Scholar
• Fox, M., & Long, D. (2006). Modelling mixed discrete-continuous domains for planning. Journal of Artificial Intelligence Research, 27, 235–297.
   Web of Science ®Google Scholar
• Frank, J., & Jónsson, A. (2003). Constraint-based attribute and interval planning. Constraints, 8(4), 339–364.
   Web of Science ®Google Scholar
• Garrett, C. R., Lozano-Pérez, T., & Kaelbling, L. P. (2014). Heuristic search for task and motion planning. Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 148–156), Portsmouth, NH.
   Google Scholar
• Gerevini, A., & Long, D. (2005a). BNF description of PDDL3.0. Unpublished manuscript from the International Planning Competition website.
   Google Scholar
• Gerevini, A., & Long, D. (2005b). Plan constraints and preferences in PDDL3 (Technical Report 2005–08-47). Dipartimento di Elettronica per l’Automazione, Università degli Studi di Brescia.
   Google Scholar
• Gerevini, A., & Long, D. (2005c). Preferences and soft constraints in PDDL3. In Proceedings of the ICAPS Workshop on Preferences and Soft Constraints in Planning (pp. 46–54), Monterey, CA.
   Google Scholar
• Ghallab, M., Howe, A., Knoblock, G., McDermott, D., Ram, A., Veloso, M., ... Wilkins, D. (1998). PDDL: The planning domain definition language. International Planning Competition Commitee.
   Google Scholar
• Haslum, P., Bonet, B., & Geffner, H. (2005). New admissible heuristics for domain-independent planning. In Proceedings of the Association for the Advancement of Artificial Intelligence (pp. 1163–1168), Pittsburgh.
   Google Scholar
• Haslum, P., Botea, A., Helmert, M., Bonet, B., & Koenig, S. (2007). Domain-independent construction of pattern database heuristics for cost-optimal planning. In Proceedings of the Association for the Advancement of Artificial Intelligence (pp. 1007–1012), Vancouver, Canada.
   Google Scholar
• Haslum, P., & Geffner, H. (2000). Admissible heuristics for optimal planning. In Proceedings of the Artificial Intelligence Planning Systems (pp. 140–149), Toronto, Canada.
   Google Scholar
• Helmert, M. (n.d.). Changes in PDDL 3.1. Unpublished manuscript International Planning Competition website.
   Google Scholar
• Helmert, M. (2006). The fast downward planning system. Journal of Artificial Intelligence Research, 26, 191–246.
   Web of Science ®Google Scholar
• Helmert, M. (2009). Concise finite-domain representations for PDDL planning tasks. Artificial Intelligence, 173(5–6), 503–535.
   Web of Science ®Google Scholar
• Helmert, M., & Domshlak, C. (2009). Landmarks, critical paths and abstractions: What is the difference anyway. In Proceedings of the International Conference on Planning and Scheduling (pp. 162–171), Thessaloniki, Greece.
   Google Scholar
• Helmert, M., Haslum, P., Hoffmann, J., & Nissim, R. (2014). Merge-and-shrink abstraction: A method for generating lower bounds in factored state spaces. Journal of the ACM, 61(3), 16–63.
   Web of Science ®Google Scholar
• Hoffmann, J. (2011). Analyzing search topology without running any search: On the connection between causal graphs and h+. Journal of Artificial Intelligence Research, 41, 155–229.
   Web of Science ®Google Scholar
• Hoffmann, J., & Nebel, B. (2001). The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14(1), 253–302.
   Google Scholar
• Hoffmann, J., Porteous, J., & Sebastia, L. (2004). Ordered landmarks in planning. Journal of Artificial Intelligence Research, 22(1), 215–278.
   Google Scholar
• Hoffmann, J., Weber, I., & Kraft, F. (2009). Planning@SAP: An application in business process management. Proceedings of the ICAPS Workshop on Scheduling and Planning Applications, Thessaloniki, Greece.
   Google Scholar
• Howey, R., Long, D., & Fox, M. (2004). VAL: Automatic plan validation, continuous effects and mixed initiative planning using PDDL. In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (pp. 294–301), Boca Raton, FL.
   Google Scholar
• Jonsson, A. (2007). The role of macros in tractable planning over causal graphs. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1936–1941), Hyderabad, India.
   Google Scholar
• Jonsson, P., & Bäckström, C. (1998). Tractable plan existence does not imply tractable plan generation. Annals of Mathematics and Artificial Intelligence, 22(3--4), 281–296.
   Web of Science ®Google Scholar
• Kambhampati, S. (2000). Planning graph as a (dynamic) CSP: Exploiting EBL, DDB and other CSP search techniques in Graphplan. Journal of Artificial Intelligence Research, 12(1), 1–34.
   Google Scholar
• Kambhampati, S. (2001). Planning as constraint satisfaction: Solving the planning graph by compiling into CSP. Artificial Intelligence, 132(2), 151–182.
   Web of Science ®Google Scholar
• Kambhampati, S., Parker, E., & Lambrecht, E. (1997). Understanding and extending Graphplan. In Proceedings of the European conference on Planning (pp. 260–272), Toulouse, France.
   Google Scholar
• Katz, M., & Domshlak, C. (2008). Structural patterns heuristics via Fork decomposition. In Proceedings of the International Conference on Automated Planning and Scheduling (pp. 182–189), Sydney, Australia.
   Google Scholar
• Kautz, H., & Selman, B. (1992). Planning as satisfiability. In Proceedings of the European Conference on Artificial Intelligence (pp. 359–363), Vienna, Austria.
   Google Scholar
• Kautz, H., & Selman, B. (1999). Unifying SAT-based and graph-based planning. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 318–325), Stockholm, Sweden.
   Google Scholar
• Kautz, H., Selman, B., & Hoffmann, J. (2006). Satplan: Planning as satisfiability. Abstracts of the 5th International Planning Competition, Ambleside, UK.
   Google Scholar
• Knoblock, C. (1994). Automatically generating abstractions for planning. Artificial intelligence, 68(2), 243–301.
   Web of Science ®Google Scholar
• Koehler, J. (1999). Handling of conditional effects and negative goals in IPP (Technical report). Freiburg University.
   Google Scholar
• Koehler, J., & Hoffmann, J. (1999). Handling of inertia in a planning system (Technical Report 122). Albert Ludwigs University.
   Google Scholar
• Koehler, J., Nebel, B., Hoffmann, J., & Dimopoulos, Y. (1997). Extending planning graphs to an ADL subset. In Proceedings of the European Conference on Planning (pp. 273–285), Toulouse, France.
   Google Scholar
• Kovacs, D. (2012). Complete BNF description of PDDL 3.1. (Technical report). Unpublished manuscript from the International Planning Competition website.
   Google Scholar
• Lagriffoul, F. (2014). Delegating geometric reasoning to the task planner. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 54–59), Portsmouth, NH.
   Google Scholar
• Lallement, R., de Silva, L., & Alami, R. (2014). HATP: An HTN planner for robotics. In Proceedings of ICAPS Workshop on Planning and Robotics (pp. 20–27), Portsmouth, NH.
   Google Scholar
• Le Berre, D., & Parrain, A. (2010). The SAT4J library, release 2.2. Journal on Satisfiability, Boolean Modeling and Computation, 7, 59–64.
   Google Scholar
• Liang, R., \ & Huang, H. (2012). Heuristics for Domain-Independent planning: A survey. Applied Mechanics and Materials, 135--136, 573–577.
   Google Scholar
• Liu, D., & McCluskey, T. L. (2001). The OCL language manual. Department of Computing and Mathematical Sciences, University of Huddersfield.
   Google Scholar
• Long, D., & Fox, M. (1999). Efficient implementation of the plan graph in STAN. Journal of Artificial Intelligence Research, 10, 87–115.
   Web of Science ®Google Scholar
• Lopez, A., & Bacchus, F. (2003). Generalizing GraphPlan by formulating planning as a CSP. In Proceedings of the International Conference on Artificial Intelligence (pp. 954–960), Acapulco, Mexico.
   Google Scholar
• Mausam, & Kolobov, A. (2012). Planning with Markov decision processes: An AI perspective. Burlington, NJ: Morgan Kaufmann & Claypool publishers.
   Google Scholar
• McDermott, D. (2005). OPT Manual Version 1.7.3 [Computer software manual].
   Google Scholar
• Nguyen, X., Kambhampati, S., & Nigenda, R. (2002). Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search. Artificial Intelligence, 135(1–2), 73–123.
   Web of Science ®Google Scholar
• Pednault, E. (1994). ADL and the state-transition model of action. Journal of Logic and Computation, 4(5), 467–512.
   Google Scholar
• Penberthy, J., & Weld, D. (1992). UCPOP: A sound, complete, partial order planner for ADL. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (pp. 103–114).
   Google Scholar
• Prud’homme, C., Fages, J. G., & Lorca, X. (2014). Choco3 Documentation. TASC, INRIA Rennes, LINA CNRS UMR 6241, COSLING S.A.S.
   Google Scholar
• Quigley, M., Gerkey, B., & Smart, W. (2015). Programming robots with ROS. Sebastopol: O’Reilly.
   Google Scholar
• Ramirez, M., Lipovetzky, N., & Muise, C. (2015). Lightweight automated planning ToolKiT. Retrieved from http://lapkt.org/
   Google Scholar
• Richter, S., Helmet, M., & Westphal, M. (2008). Landmarks revisited. In Proceedings of the Association for the Advancement of Artificial Intelligence (pp. 975–982), Chicago, IL.
   Google Scholar
• Richter, S., & Westphal, M. (2010). The LAMA planner: Guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research, 39, 127–177.
   Web of Science ®Google Scholar
• Rintanen, J. (2012). Planning as satisfiability: Heuristics. Artificial Intelligence, 193, 45–86.
   Web of Science ®Google Scholar
• Rintanen, J. (2014). Madagascar: Scalable planning with SAT. Proceedings of the International Competition of Planning, Portsmouth, NH.
   Google Scholar
• Rintanen, J., Heljanko, K., & Niemelä, I. (2006). Planning as satisfiability: Parallel plans and algorithms for plan search. Artificial Intelligence, 170(12--13), 1031–1080.
   Web of Science ®Google Scholar
• Robinson, N., Gretton, C., Pham, D. N., & Sattar, A. (2009). SAT-based parallel planning using a split representation of actions. In Proceedings of the International on Automated Planning and Scheduling (pp. 281–288), Thessaloniki, Greece .
   Google Scholar
• Salnitri, M., Paja, E., & Giorgini, P. (2015). Maintaining secure business processes in light of socio-technical system’s evaluation (Technical report). DISI-University of Trento.
   Google Scholar
• Sanner, S. (2010). Relational dynamic influence diagram language (RDDL): Language description. Unpublished manuscript from the International Planning Competition website.
   Google Scholar
• Srivastava, S., Riano, L., Russell, S., & Abbeel, P. (2013). Using classical planners for tasks with continuous operators in robotics. In Proceedings of the ICAPS Workshop on Planning and Robotics (pp. 27–35), Rome, Italy.
   Google Scholar
• Thomas, J. M., & Young, M. R. (2006). Author in the loop: Using mixed-initiative planning to improve interactive narrative. Proceedings of the ICAPS Workshop on AI Planning for Synthetic Characters and Computer Games, Ambleside, UK.
   Google Scholar
• Triantafillou, E., Baier, J., & McIlraith, S. (2015). A unifying framework for planning with LTL and regular expressions. In Proceedings of the ICAPS Workshop on Model-Checking and Automated Planning (pp. 23–31), Jerusalem, Israel.
   Google Scholar
• van Beek, P., & Chen, X. (1999). CPlan: A constraint programming approach to planning. In Proceedings of the Association for the Advancement of Artificial Intelligence (pp. 585–590), Orlando, FL.
   Google Scholar
• Wehrle, M., & Rintanen, J. (2007). Planning as satisfiability with relaxed ∃-step plans. In Proceedings of the Australian Joint Conference on Artificial Intelligence (pp. 244–253), Gold Coast, Australia.
   Google Scholar
• Williams, B., & Nayak, P. (1997). A reactive planner for a model-based executive. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 150–159), Hyderabad, India.
   Google Scholar
• Younes, H., & Littman, M. (2004). PPDDL 1.0: An extension to PDDL for expressing planning domains with probabilistic effects (Technical Report CMU-CS-04-167). Carnegie Mellon University.
   Google Scholar
• Zaman, S., Steinbauer, G., Maurer, J., Lepej, P., & Uran, S. (2013). An integrated model-based diagnosis and repair architecture for ROS-based robot systems. In Proceedings of the International Conference on Robotics and Automation (pp. 482–489), Karlsruhe, Germany.
   Google Scholar