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Aler R., Borrajo D., Isasi P.2002. Using genetic programming to learn and improve control knowledge. Artificial Intelligence141(1–2), 29–56.

Amir E., Chang A.2008. Learning partially observable deterministic action models. Journal of Artificial Intelligence Research33, 349–402.

Bacchus F., Kabanza F.2000. Using temporal logics to express search control knowledge for planning. Artificial Intelligence116(1–2), 123–191.

Barto A., Duff M.1994. Monte carlo matrix inversion and reinforcement learning. Advances in Neural Information Processing Systems 6, 687–694.

Bellingham J., Rajan K.2007. Robotics in remote and hostile environments. Science318(5853), 1098–1102.

Bellman R., Kalaba R.1965. Dynamic Programming and Modern Control Theory. Academic Press.

Benson S. S.1997. Learning Action Models for Reactive Autonomous Agents. PhD thesis, Stanford University.

Bergmann R., Wilke W.1996. PARIS: flexible plan adaptation by abstraction and refinement. In Workshop on Adaptation in Case-Based Reasoning, ECAI-96.

Bertsekas D. P.1995. Dynamic Programming and Optimal Control. Athena Scientific.

Bertsekas D. P., Tsitsiklis J. N.1996. Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3). Athena Scientific.

Blockeel H., De Raedt L.1998. Top-down induction of first-order logical decision trees. Artificial Intelligence101, 285–297.

Blockeel H., Raedt L. D., Ramong J.1998. Top-down induction of clustering trees. In Proceedings of the 15th International Conference on Machine Learning, San Francisco, CA, USA.

Blum A. L., Furst M. L.1995. Fast planning through planning graph analysis. Artificial Intelligence90(1), 1636–1642.

Bonet B., Geffner H.2001. Planning as heuristic search. Artificial Intelligence129(1–2), 5–33.

Borrajo D., Veloso M.1997. Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review Journal—Special Issue on Lazy Learning11(1–5), 371–405.

Botea A., Enzenberger M., Mller M., Schaeffer J.2005a. Macro-FF: improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research24, 581–621.

Botea A., Müller M., Schaeffer J.2005b. Learning partial-order macros from solutions. In ICAPS 2005. Proceedings of the 15th International Conference on Automated Planning and Scheduling, Biundo, S., Myers, K. & Rajan, K. (eds). Monterey, California, 231–240.

Botea A., Müller M., Schaeffer J.2007. Fast planning with iterative macros. In Proceedings of the International Joint Conference on Artificial Intelligence IJCAI-07, 1828–1833.

Boutilier C., Reiter R., Price B.2001. Symbolic dynamic programming for first-order MDPs. In International Joint Conference on Artificial Intelligence, Seattle, Washington, USA.

Brazdil P., Giraud-Carrier C., Soares C., Vilalta R.2009. Metalearning: Applications to Data Mining—Cognitive Technologies. Springer.

Bresina J. L., Jansson A. K., Morris P. H., Rajan K.2005. Mixed-initiative activity planning for mars rovers. In IJCAI, Edinburgh, Scotland, UK, 1709–1710.

Bui H. H., Venkatesh S., West G.2002. Policy recognition in the abstract hidden Markov model. Journal of Artificial Intelligence Research17, 451–499.

Bulitko V., Lee G.2006. Learning in real-time search: a unifying framework. Journal of Artificial Intelligence Research25, 119–157.

Bylander T.1991. Complexity results for planning. In International Joint Conference on Artificial Intelligence, IJCAI-91, Sydney, Australia.

Bylander T.1994. The computational complexity of propositional STRIPS planning. Artificial Intelligence69(1–2), 165–204.

Castillo L., Fdez-Olivares J., García-Pérez O., Palao F.2006. Bringing users and planning technology together. Experiences in SIADEX. In International Conference on Automated Planning and Scheduling (ICAPS 2006), Cumbria, UK.

Charniak E., Goldman R. P.1993. A bayesian model of plan recognition. Artificial Intelligence64(1), 53–79.

Cohen W. W.1990. Learning approximate control rules of high utility. In International Conference on Machine Learning, Austin, Texas, USA.

Coles A., Smith A.2007. Marvin: a heuristic search planner with online macro-action learning. Journal of Artificial Intelligence Research28, 119–156.

Cortellessa G., Cesta A.2006. Evaluating mixed-initiative systems: an experimental approach. In Proceedings of the 16th International Conference on Automated Planning & Scheduling, ICAPS-06, Cumbria, UK.

Cresswell S., McCluskey T. L., West M.2009. Acquisition of object-centred domain models from planning examples. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), Thessaloniki, Greece.

Croonenborghs T., Driessens K., Bruynooghe M.2007a. Learning relational options for inductive transfer in relational reinforcement learning. In Proceedings of the 17th Conference on Inductive Logic Programming, Corvallis, OR, USA.

Croonenborghs T., Ramon J., Blockeel H., Bruynooghe M.2007b. Online learning and exploiting relational models in reinforcement learning. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. AAAI Press, 726–731.

Cussens J.2001. Parameter estimation in stochastic logic programs. Machine Learning44(3), 245–271.

Dawson C., Silklossly L.1977. The role of preprocessing in problem solving system. In International Joint Conference on Artificial Intelligence, IJCAI-77, Cambridge, MA, USA, 465–471.

de la Rosa T., García-Olaya A., Borrajo D.2007. Using cases utility for heuristic planning improvement. In International Conference on Case-Based Reasoning, Belfast, Northern Ireland.

de la Rosa T., Jiménez S., Borrajo D.2008. Learning relational decision trees for guiding heuristic planning. In International Conference on Automated Planning and Scheduling (ICAPS 08), Sydney, Australia.

de la Rosa T., Jiménez S., García-Durán R., Fernández F., García-Olaya A., Borrajo D.2009. Three relational learning approaches for lookahead heuristic planning. In Working Notes of the ICAPS'09 Workshop on Planning and Learning, Thessaloniki, Greece.

Driessens K., Matwin S.2004. Integrating guidance into relational reinforcement learning. Machine Learning57, 271–304.

Driessens K., Ramon J.2003. Relational instance based regression for relational reinforcement learning. In International Conference on Machine Learning, Washington, DC, USA.

Dzeroski S., Raedt L. D., Driessens K.2001. Relational reinforcement learning. Machine Learning43, 7–52.

Edelkamp S.2002. Symbolic pattern databases in heuristic search planning. In International Conference on Automated Planning and Scheduling, Toulouse, France.

Ernst G. W., Newell A.1969. GPS: A Case Study in Generality and Problem Solving, ACM Monograph Series. Academic Press.

Erol K., Nau D. S., Subrahmanian V. S.1992. On the complexity of domain-independent planning. Artificial Intelligence56, 223–254.

Estlin T. A., Mooney R. J.1996. Hybrid learning of search control for partial-order planning. In In New Directions in AI Planning. IOS Press, 115–128.

Etzioni O.1993. Acquiring search-control knowledge via static analysis. Artificial Intelligence62(2), 255–301.

Ferguson G., Allen J. F., Miller B.1996. Trains-95: towards a mixed-initiative planning assistant. In International Conference on Artificial Intelligence Planning Systems, AIPS96, Edinburgh, UK. AAAI Press, 70–77.

Fern A., Yoon S., Givan R.2004. Learning domain-specific control knowledge from random walks. In International Conference on Automated Planning and Scheduling, Whistler, Canada, 191–199.

Fern A., Yoon S. W., Givan R.2006. Approximate policy iteration with a policy language bias: solving relational Markov decision processes. Journal of Artificial Intelligence Research25, 75–118.

Fikes R., Hart P., Nilsson N. J.1972. Learning and executing generalized robot plans. Artificial Intelligence3, 251–288.

Fikes R., Nilsson N. J.1971. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence2, 189–208.

Florez J. E., Garca J., Torralba A., Linares C., Garca-Olaya A., Borrajo D.2010. Timiplan: an application to solve multimodal transportation problems. In Proceedings of SPARK, Scheduling and Planning Applications woRKshop, ICAPS'10, Toronto, Canada.

Fox M., Long D.2003. PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research, 61–124.

Fuentetaja R., Borrajo D.2006. Improving control-knowledge acquisition for planning by active learning. In European Conference on Learning, Berlin, Germany, 138–149.

García-Durán R., Fernández F., Borrajo D.2006. Combining macro-operators with control knowledge. In ILP, Santiago de Compostela, Spain.

García-Durán R., Fernández F., Borrajo D. (2012). A prototype-based method for classification with time constraints: a case study on automated planning. Pattern Analysis and Applications Journal15(3), 261–277.

García-Martínez R., Borrajo D.2000. An integrated approach of learning, planning, and execution. Journal of Intelligent and Robotics Systems29, 47–78.

Gartner T., Driessens K., Ramon J.2003. Graph kernels and Gaussian processes for relational reinforcement learning. In International Conference on Inductive Logic Programming, ILP 2003, Szeged, Hungary.

Gerevini A., Saetti A., Vallati M.2009a. An automatically configurable portfolio-based planner with macro-actions: PbP. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), Thessaloniki, Greece.

Gerevini A. E., Haslum P., Long D., Saetti A., Dimopoulos Y.2009b.Deterministic planning in the fifth international planning competition: Pddl3 and experimental evaluation of the planners. Artificial Intelligence173(5–6), 619–668.

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

Gil Y.1992. Acquiring Domain Knowledge for Planning by Experimentation. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh.

Gretton C., Thiébaux S.2004. Exploiting first-order regression in inductive policy selection. In Conference on Uncertainty in Artificial Intelligence, Banff, Canada.

Helmert M.2009. Concise finite-domain representations for pddl planning tasks. Artificial Intelligence.

Hernández C., Meseguer P.2007. Improving LRTA*(k). In International Joint Conference on Artificial Intelligence, IJCAI-07, Hyderabad, India, 2312–2317.

Hoffmann J., Nebel B.2001a. The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research14, 253–302.

Hoffmann J., Nebel B.2001b. The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research14, 253–302.

Hogg C., Muñoz-Avila H., Kuter U.2008. HTN-MAKER: learning HTNs with minimal additional knowledge engineering required. In National Conference on Artificial Intelligence (AAAI'2008), Chicago, Illinois, USA.

Hogg C., Kuter U., Muñoz-Avila H.2009. Learning hierarchical task networks for nondeterministic planning domains. In International Joint Conference on Artificial Intelligence, IJCAI-09, Pasadena, CA, USA.

Howe A. E., Dahlman E., Hansen C., Scheetz M., Mayrhauser A. V.1999. Exploiting competitive planner performance. In Proceedings of the 5th European Conference on Planning, Durham, UK.

Ilghami O., Nau D. S., Muñoz-Avila H.2002. CaMeL: learning method preconditions for HTN planning. In Proceedings of the 6th International Conference on AI Planning and Scheduling, Toulouse, France. AAAI Press, 131–141.

Ilghami O., Muñoz-Avila H., Nau D. S., Aha D. W.2005. Learning approximate preconditions for methods in hierarchical plans. In International Conference on Machine Learning, Bonn, Germany.

Ilghami O., Nau D. S., Muñoz-Avila H.2006. Learning to do HTN planning. In International Conference on Automated Planning and Scheduling, ICAPS 2006, Cumbria, UK.

Jaeger M.1997. Relational bayesian networks. In Conference on Uncertainty in Artificial Intelligence, Rhode Island, Providence, USA.

Jiménez S., Fernández F., Borrajo D.2008. The PELA architecture: integrating planning and learning to improve execution. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, USA.

Kaelbling L. P., Littman M. L., Moore A. P.1996. Reinforcement learning: a survey. Journal of Artificial Intelligence Research4, 237–285.

Kambhampati S.2007. Model-lite planning for the web age masses: the challenges of planning with incomplete and evolving domain models. In: Senior Member Track of the AAAI. AAAI Press/MIT Press.

Kambhampati S., Hendler J. A.1992. A validation structure-based theory of plan modification and reuse. Artificial Intelligence Journal55, 193–258.

Keller R.1987. The Role of Explicit Contextual Knowledge in Learning Concepts to Improve Performance. PhD thesis, Rutgers University.

Kersting K., Raedt L. D.2001. Towards combining inductive logic programming with Bayesian networks. In International Conference on Inductive Logic Programming, Strasbourg, France, 118–131.

Khardon R.1999. Learning action strategies for planning domains. Artificial Intelligence113, 125–148.

Kittler J.1998. Combining classifiers: A theoretical framework. Pattern Analysis and Application1(1), 18–27.

Korf R. E.1985. Macro-operators: a weak method for learning. Artificial Intelligence26, 35–77.

Korf R. E.1990. Real-time heuristic search. Artificial Intelligence42(2–3), 189–211.

Lanchas J., Jiménez S., Fernández F., Borrajo D.2007. Learning action durations from executions. In Working notes of the ICAPS'07 Workshop on AI Planning and Learning, Rhode Island, Providence, USA.

Larkin J., Reif F., Carbonell J.1988. FERMI: a flexible expert reasoner with multi-domain inference. Cognitive Science12(1), 101–138.

Leckie C., Zukerman I.1991. Learning search control rules for planning: an inductive approach. In Proceedings of the International Workshop on Machine Learning. Morgan Kaufmann, 422–426.

Martin M., Geffner H.2000. Learning generalized policies in planning using concept languages. In International Conference on Artificial Intelligence Planning Systems, AIPS00, Breckenridge, USA.

Mcallester D., Givan R.1989. Taxonomic syntax for first order inference. Journal of the ACM40, 289–300.

McCluskey T. L.1987. Combining weak learning heuristics in general problem solvers. In IJCAI'87: Proceedings of the 10th International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc., Milan, Italy, 331–333.

McGann C., Py F., Rajan K., Ryan J., Henthorn R.2008. Adaptive control for autonomous underwater vehicles. In National Conference on Artificial Intelligence (AAAI'2008), Chicago, Illinois, USA.

Mehta N., Natarajan S., Tadepalli P., Fern A.2008. Transfer in variable-reward hierarchical reinforcement learning. Machine Learning73(3), 289–312.

Minton S.1988. Learning Effective Search Control Knowledge: An Explanation-Based Approach. Kluwer Academic Publishers.

Mitchell T. M.1997. Machine Learning. McGraw-Hill.

Mitchell T., Utgoff T., Banerji R.1982. Learning problem solving heuristics by experimentation. In Machine Learning: An Artificial Intelligence Approach, Michalski, R. S., Carbonell, J. G. & Michell, T. M. (eds). Morgan Kaufmann.

Mourão K., Petrick R. P. A., Steedman M.2008. Using kernel perceptrons to learn action effects for planning. In Proceedings of the International Conference on Cognitive Systems (CogSys 2008), Karlsruhe, Germany.

Mourão K., Petrick R. P. A., Steedman M.2010. Learning action effects in partially observable domains. In European Conference on Artificial Intelligence, Barcelona, Spain.

Muggleton S.1995. Stochastic logic programs. In International Workshop on Inductive Logic Programming, Leuven, Belguim.

Muise C., McIlraith S., Baier J. A., Reimer M.2009. Exploiting N-gram analysis to predict operator sequences. In 19th International Conference on Automated Planning and Scheduling (ICAPS), Thessaloniki, Greece.

Muñoz-Avila Aha H., Breslow D. & Nau L.1999. HICAP: An interactive case based planning architecture and its application to noncombatant evacuation operations. In Conference on Innovative Applications of Artificial Intelligence, IAAI-99, Orlando, Florida, USA.

Nau D. S., Smith S. J., Erol K.1998. Control strategies in htn planning: theory versus practice. In In AAAI-98/IAAI-98 Proceedings, Madison, Wisconsin. USA.

Nau D., Ilghami O., Kuter U., Murdock J. W., Wu D., Yaman F.2003. SHOP2: an HTN planning system. Journal of Artificial Intelligence Research20, 379–404.

Nayak P., Kurien J., Dorais G., Millar W., Rajan K., Kanefsky R.1999. Validating the DS-1 remote agent experiment. In Artificial Intelligence, Robotics and Automation in Space.

Newton M. A. H., Levine J., Fox M., Long D.2007. Learning macro-actions for arbitrary planners and domains. In International Conference on Automated Planning and Scheduling, Providence, USA.

Nilsson N. J.1984. Shakey the Robot. Technical Report 323, AI Center, SRI International.

Oates T., Cohen P. R.1996. Searching for planning operators with context-dependent and probabilistic effects. In National Conference on Artificial Intelligence, Portland, Oregon, USA.

Otterlo M. V.2009. The Logic of Adaptive Behavior: Knowledge Representation and Algorithms for Adaptive Sequential Decision Making under Uncertainty in First-Order and Relational Domains. IOS Press.

Pasula H. M., Zettlemoyer L. S., Kaelbling L. P.2007. Learning symbolic models of stochastic domains. Journal of Artificial Intelligence Research29, 309–352.

Porteous J., Sebastia L.2004. Ordered landmarks in planning. Journal of Artificial Intelligence Research22, 215–278.

Quinlan J., Cameron-Jones R.1995. Introduction of logic programs: FOIL and related systems. NewGeneration Computing, Special issue on Inductive Logic Programming13(3–4), 287–312.

Raedt L. D.2008. Logical and Relational Learning. Springer.

Ramirez M., Geffner H.2009. Plan recognition as planning. In IJCAI'09: Proceedings of the 21st International Jont Conference on Artifical Intelligence, Pasadena, CA, USA.

Ramirez M., Geffner H.2010. Probabilistic plan recognition using off-the-shelf classical planners. In National Conference on Artificial Intelligence (AAAI'2010), Atlanta, Georgia, USA.

Reynolds S. I.2002. Reinforcement Learning with Exploration. PhD thesis, The University of Birmingham, UK.

Richardson M., Domingos P.2006. Markov logic networks. Machine Learning62, 107–136.

Rivest R. L.1987. Learning decision lists. Machine Learning2(3), 229–246.

Sanner S., Boutilier C.2005. Approximate linear programming for first-order mdps. In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, Edinburgh, Scotland, UK, 509–517.

Sanner S., Boutilier C.2006. Practical linear value-approximation techniques for first-order MDPs. In Proceedings of the 22nd Conference in Uncertainty in Artificial Intelligence, Cambridge, MA, USA.

Sanner S., Kersting K.2010. Symbolic dynamic programming for first-order pomdps. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-10), Fox, M. & Poole, D. (eds). Atlanta, Georgia, USA, AAAI Press.

Serina I.2010. Kernel functions for case-based planning. Artificial Intelligence174(16–17), 1369–1406.

Shavlik J. W.1989. Acquiring recursive and iterative concepts with explanation-based learning. In Machine Learning.

Shen W., Simon H. A.1989. Rule creation and rule learning through environmental exploration. In International Joint Conference on Artificial Intelligence, IJCAI-89, Detroit, Michigan, USA, 675–680.

Srivastava S., Immerman N., Zilberstein S.2008. Learning generalized plans using abstract counting. In National Conference on Artificial Intelligence (AAAI'2008), Chicago, Illinois, USA.

Strehl A. L., Littman M. L.2005. A theoretical analysis of model-based interval estimation. In Proceedings of the 21nd International Conference on Machine Learning (ICML-05), Bonn, Germany, 857–864.

Sutton R. S., Barto A. G.1998. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). The MIT Press.

Taylor M. E., Stone P.2007. Cross-domain transfer for reinforcement learning. In International Conference on Machine Learning, ICML, Corvallis, OR, USA.

Theocharous G., Kaelbling L. P.2003. Approximate planning in POMDPs with macro-actions. In Proceedings of Advances in Neural Information Processing Systems 16, Whistler, Canada.

Thiébaux S., Hoffmann J., Nebel B.2005. In defense of PDDL axioms. Artificial Intelligence168(1–2), 38–69.

Veloso M. M., Carbonell J. G.1993. Derivational analogy in prodigy: automating case acquisition, storage, and utilization. Machine Learning10, 249–278.

Veloso M. M., Pérez M. A., Carbonell J. G.1990. Nonlinear planning with parallel resource allocation. In Proceedings of the DARPA Workshop on Innovative Approaches to Planning, Scheduling, and Control, San Diego, CA, USA, Morgan Kaufmann, 207–212.

Vrakas D., Tsoumakas G., Bassiliades N., Vlahavas I. P.2005. HAPRC: an automatically configurable planning system. AI Communications18(1), 41–60.

Walsh T. J., Littman M. L.2008. Efficient learning of action schemas and web-service descriptions. In AAAI'08: Proceedings of the 23rd National Conference on Artificial Intelligence, Chicago, Illinois, USA. AAAI Press, 714–719.

Wang X.1994. Learning planning operators by observation and practice. In International Conference on AI Planning Systems, AIPS-94, Chicago, Illinois, USA.

Wang C., Joshi S., Khardon R.2007. First order decision diagrams for relational MDPs. In International Joint Conference on Artificial Intelligence, IJCAI-07, Hyderabad, India.

Watkins C. J. C. H.1989. Learning from Delayed Rewards. PhD thesis, King's College, Oxford.

Wiering M.1999. Explorations in efficient reinforcement learning. PhD thesis, University of Amsterdam IDSIA, the Netherlands.

Winner E., Veloso M.2003. DISTILL: towards learning domain-specific planners by example. In International Conference on Machine Learning, ICML'03, Washington, DC, USA.

Xu Y., Fern A., Yoon S. W.2007. Discriminative learning of beam-search heuristics for planning. In International Joint Conference on Artificial Intelligence, Hyderabad, India.

Yang Q., Wu K., Jiang Y.2007. Learning action models from plan traces using weighted MAX-SAT. Artificial Intelligence Journal171, 107–143.

Yoon S., Kambhampati S.2007. Towards model-lite planning: a proposal for learning and planning with incomplete domain models. In ICAPS2007 Workshop on Artificial Intelligence Planning and Learning, Providence, USA.

Yoon S., Fern A., Givan R.2002. Inductive policy selection for first-order MDPs. In Conference on Uncertainty in Artificial Intelligence, UAI02, Alberta, Edmonton, Canada.

Yoon S., Fern A., Givan R.2006. Learning heuristic functions from relaxed plans. In International Conference on Automated Planning and Scheduling (ICAPS-2006), Cumbria, UK.

Yoon S., Fern A., Givan R.2007. Using learned policies in heuristic-search planning. In International Joint Conference on Artificial Intelligence, Hyderabad, India.

Yoon S., Fern A., Givan R.2008. Learning control knowledge for forward search planning. Journal of Machine Learning Research9, 683–718.

Younes H., Littman M. L., Weissman D., Asmuth J.2005. The first probabilistic track of the international planning competition. Journal of Artificial Intelligence Research24, 851–887.

Zelle J., Mooney R.1993. Combining FOIL and EBG to speed-up logic programs. In International Joint Conference on Artificial Intelligence. IJCAI-93, Chambéry, France.

Zhuo H., Li L., Yang Q., Bian R.2008. Learning action models with quantified conditional effects for software requirement specification. In ICIC '08: Proceedings of the 4th International Conference on Intelligent Computing, Shanghai, China. Springer-Verlag, 874–881.

Zimmerman T., Kambhampati S.2003. Learning-assisted automated planning: looking back, taking stock, going forward. AI Magazine24, 73–96.