|
Aler R. and Borrajo D.2002 On control knowledge acquisition by exploiting human-computer interaction. In Ghallab M., Hertzberg J. and Traverso P. (eds.), Proceedings of the Sixth International Conference on Artificial Intelligence Planning Systems (AIPS-02). Toulouse, France: AAAI Press, pp. 112–120. |
|
Aler R., 2000 Knowledge representation issues in control knowledge learning. In Langley P. (ed.), Proceedings of the Seventeenth International Conference on Machine Learning, ICML’00. Stanford, CA(USA): Morgan Kaufmann, pp. 1–8. |
|
Aler R., 2002Using genetic programming to learn and improve control knowledge. Artificial Intelligence141(1–2), 29–56. |
|
Ambite J.L., 2000 Learning plan rewriting rules. In Steve Chien, Subbarao Kambhampati & Graig, A. (eds.), Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling. Breckenbridge, CO, USA: pp. 14–17. |
|
Arias J.D., 2005 Using ontologies for planning tourist visits. In Working notes of the ICAPS’0 5Workshop on Role of Ontologies in Planning and Scheduling. Monterey, CA (EEUU): AAAI, AAAI Press, pp. 52–59. |
|
Bacchus F. and Kabanza F.2000Using temporal logics to express search control knowledge for planning. Artificial Intelligence116, 123–191. |
|
Bäckström C.1992 Computational complexity of reasoning about plans. PhD thesis, Linkoping University, Linkoping, Sweden. |
|
Blum A.L. & Furst M. L.1995 Fast planning through planning graph analysis. In Mellish C. S. (ed.), Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI-95. Montréal, Canada: Morgan Kaufmann, pp. 1636–1642. |
|
Bonet B. and Geffner H.2001Planning as heuristic search. Artificial Intelligence129(1–2), 5–33. |
|
Borrajo D. and Veloso M.1997Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review Journal. Special Issue on Lazy Learning11(1–5), 371–405. Also in the book Lazy learning, Aha D. (ed.), Kluwer Academic Publishers, May 1997, ISBN 0-7923-4584-3. |
|
Borrajo D., 1999 Multistrategy relational learning of heuristics for problem solving. In Bramer M., Macintosh A. and Coenen F. (eds.), Research and Development in Intelligent Systems XVI. Proceedings of Expert Systems 99, The 19th SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, BCS Conference Series. Cambridge, England: Springer-Verlag, pp. 57–71. |
|
Borrajo D., 2001 Quality-based Learning for Planning. In Working notes of the IJCAI’01 Workshop on Planning with Resources. Seattle, WA (USA): IJCAI Press, pp. 9–17. |
|
Botea A., 2005 Learning partial-order macros from solutions. In Proceedings of ICAPS’05, Monterrey (USA). |
|
Bylander T.1994The computational complexity of propositional STRIPS planning. Artificial Intelligence69(1–2), 165–204. |
|
Carbonell J.G., 1992 PRODIGY4.0: the manual and tutorial. Technical Report CMU-CS-92-150, Department of Computer Science, Carnegie Mellon University. |
|
Castillo L., 2001Mixing expressiveness and efficiency in a manufacturing planner. Journal of Experimental and Theoretical Artificial Intelligence13, 141–162. |
|
Cesta A., 2002A constrained-based method for project scheduling with time windows. Journal of Heuristics8, 109–136. |
|
Cortellesa G. and Cesta A.2006 Feature evaluation in mixed-initiative systems: an experimental approach. In Derek Long D.B., Smith S. and McCluskey L. (eds.), Proceedings of ICAPS’06. Ambleside (UK): AAAI Press. |
|
Currie K. and Tate A.1991O-plan: the open planning architecture. Artificial Intelligence52(1), 49–86. |
|
Estlin T.A. and Mooney R.J.1997 Learning to improve both efficiency and quality of planning. In Pollack M. (ed.), Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97). Nagoya, Japan: Morgan Kaufmann, pp. 1227–1232. |
|
Etzioni O. and Minton S.1992 Why EBL produces overly-specific knowledge: a critique of the prodigy approaches. In Proceedings of the Ninth International Conference on Machine Learning. Aberdeen, Scotland: Morgan Kaufmann, pp. 137–143. |
|
Fernández S., 2004 Using previous experience for learning planning control knowledge. In Barr V. and Markov Z. (eds.), Proceedings of the Seventeen International Florida Artificial Intelligence (FLAIRS04). Miami Beach, FL (USA): AAAI Press, pp. 713–718. |
|
Fernández S., 2005Machine learning in hybrid hierarchical and partial-order planners for manufacturing domains. Applied Artificial Intelligence19(8), 783–809. |
|
Fikes R.E., 1972Learning and executing generalized robot plans. Artificial Intelligence3, 251–288. |
|
Ghallab M., 2004Automated task planning. theory & practice. San Francisco: Morgan Kaufmann. |
|
Gratch J. and DeJong G.1992 COMPOSER: a probabilistic solution to the utility problem in speed-up learning. In Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 235–240. |
|
Hoffmann J. and Nebel B.2001The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research14, 253–302. |
|
Huang Y.-C., 2000 Learning declarative control rules for constraint-based planning. In Langley P. (ed.), Proceedings of the Seventeenth International Conference on Machine Learning, ICML’00, Stanford, CA (USA). |
|
Joseph R.L.1989 Graphical knowledge acquisition. In Proceedings of the 4th Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada. |
|
Kambhampati S.1989 Flexible reuse and modification in hierarchical planning: a validation structure based approach. PhD thesis, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, MD. |
|
Kambhampati S.2000Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in graph-plan. Journal of Artificial Intelligence Research12, 1–34. |
|
Khardon R.1999Learning action strategies for planning domains. Artificial Intelligence113(1–2), 125–148. |
|
Knoblock C.A., 1991 Integrating abstraction and explanation based learning in PRODIGY. In Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 541–546. |
|
Korf R.E.1985Macro-operators: a weak method for learning. Artificial Intelligence26, 35–77. |
|
McCluskey L., 2003a Knowledge engineering for planning ROADMAP. In Lee McCluskey (ed.) The PLANET Final Report to the EC, November 2000. |
|
McCluskey T.L.1987 Combining weak learning heuristics in general problem solvers. In Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI’87). Milan, Italy: Morgan Kaufman. |
|
McCluskey T.L.1989 Explanation-based and similarity-based heuristic acquisition in a general planner. In Proceedings of the 4th European Working Session on Learning. London: Pitman. |
|
McCluskey T.L., 2003b GIPO II: HTN planning in a tool-supported knowledge engineering environment. In Proceedings of ICAPS’03. Trento (Italia): AAAI Press. |
|
McCluskey T.L. & Porteous J.M.1997Engineering and compiling planning domain models to promote validity and efficiency. Artificial Intelligence95(1), 1–65. |
|
Minton S.1988Learning Effective Search Control Knowledge: An Explanation-Based Approach. Boston, MA: Kluwer Academic Publishers. |
|
Minton S., 1989 PRODIGY 2.0: the manual and tutorial. Technical Report CMU-CS-89-146, School of Computer Science, Carnegie Mellon University. |
|
Mitchell T.M., 1986Explanation-based generalization: a unifying view. Machine Learning1, 47–80. |
|
Myers K. and Wilkins D.1997 The act-editor user’s guide: a manual for version 2.2. Technical Report, SRI. |
|
Nau D., 2003SHOP2: an HTN planning system. Journal of Artificial Intelligence Research20, 379–404. |
|
Qu Y. and Kambhampati S.1995 Learning search control rules for plan-space planners: factors affecting the performance. Technical Report, Arizona State University. |
|
Rodríguez-Moreno M.D., 2004a An AI planning-based tool for scheduling satellite nominal operations. AI Magazine25(4), 9–27. |
|
Rodríguez-Moreno M.D., 2004b IPSS: a problem solver that integrates P&S’. In Third Italian Workshop on Planning and Scheduling. |
|
Rodríguez-Moreno M.D., 2004c IPSS: a hybrid reasoner for planning and scheduling. In de Mántaras R. L. and Saitta L. (eds.), Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004).Valencia (Spain): IOS Press, pp. 1065–1066. |
|
Upal M.A. and Elio R.2000 Learning search control rules versus rewrite rules to improve plan quality. In Proceedings of the Thirteenth Canadian Conference on Artificial Intelligence. New York: Springer-Verlag, pp. 240–253. |
|
Veloso M.1994Planning and Learning by Analogical Reasoning. New York, USA: Springer Verlag. |
|
Veloso M., 1995Integrating planning and learning: the PRODIGY architecture. Journal of Experimental and Theoretical AI7, 81–120. |
|
Wang X.1994 Learning planning operators by observation and practice. In Proceedings of the Second International Conference on AI Planning Systems, AIPS-94. Chicago, IL: AAAI Press, CA, pp. 335–340. |
|
Yang Q., 2005 Learning action models from plan examples with incomplete knowledge. In Proceedings of ICAPS’05, Monterrey, USA. |
|
Zimmerman T. and Kambhampati S.2003Learning-assisted automated planning: looking back, taking stock, going forward. AI Magazine24(2), 73–96. |