The AI Domain Definition Language (AIDDL) for Integrated Systems

1. Bratko, I.: Prolog Programming for Artificial Intelligence. Addison Wesley (2000)

   Google Scholar 

2. Coles, A.J., Coles., A.I.: PDDL+ planning with events and linear processes. In: Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS) (2014)

   Google Scholar 

3. Coles, A., Coles, A.J.: Marvin: a heuristic search planner with online macro-action learning. J. Artif. Intell. Res. 28, 119–156 (2007)

   Article  MATH  Google Scholar 

4. Cox, M.T., Dannenhauer, D.: Goal transformation and goal reasoning. In: Proceedings of the 4th Workshop on Goal Reasoning at IJCAI-2016 (2016)

   Google Scholar 

5. Fox, M., Long, D.: PDDL2.1: an extension to pddl for expressing temporal planning domains. J. Artif. Intell. Res. 20, 61–124 (2003)

   Google Scholar 

6. Gerevini, A., Long, D.: Plan Constraints and Preferences in PDDL3. Department of Electronics for Automation, University of Brescia, Ital, Technical report (2005)

   Google Scholar 

7. Gerevini, A.E., Saetti, A., Vallati, M.: An automatically configurable portfolio-based planner with macro-actions: Pbp. In: Proceedings of the 19th International Conference on International Conference on Automated Planning and Scheduling (ICAPS), pp. 350–353. ICAPS 2009, AAAI Press (2009)

   Google Scholar 

8. Ghallab, M., et al.: PDDL - the planning domain definition language. Technical report, CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control (1998)

   Google Scholar 

9. Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Morgan Kaufmann (2004)

   Google Scholar 

10. Gil, Y.: Acquiring domain knowledge for planning by experimentation. Ph.D. thesis, CMU, Pittsburgh, PA, USA (1992)

    Google Scholar 

11. Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. 26(1), 191–246 (2006)

    Article  MATH  Google Scholar 

12. Hoffmann, J.: FF: the fast-forward planning system. AI Magazine 22, 57–62 (2001)

    Google Scholar 

13. Jiménez, S., De La Rosa, T., Fernández, S., Fernández, F., Borrajo, D.: A review of machine learning for automated planning. Knowl. Eng. Rev. 27(4), 433–467 (2012)

    Article  Google Scholar 

14. Mitchell, T.M.: Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edn. (1997)

    Google Scholar 

15. Shen, W.M., Simon, H.A.: Rule creation and rule learning through environmental exploration. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 675–680 (1989)

    Google Scholar 

16. Thayer, J.T., Dionne, A.J., Ruml, W.: Learning inadmissible heuristics during search. In: Bacchus, F., Domshlak, C., Edelkamp, S., Helmert, M. (eds.) Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS). AAAI (2011)

    Google Scholar 

17. Tong, S.: Active learning: theory and applications. Ph.D. thesis, Stanford University (2001)

    Google Scholar 

18. Vattam, S., Klenk, M., Molineaux, M., Aha, D.W.: Breadth of approaches to goal reasoning : a research survey. Goal Reasoning: Papers from the ACS Workshop, p. 111 (2013)

    Google Scholar 

19. Wang, X.: Learning planning operators by observation and practice. In: International Conference on Artificial Intelligence Planning Systems (1994)

    Google Scholar 

Download references