B. Bonet and H. Geffner, Labeled RTDP: Improving the convergence of real-time dynamic programming, Proceedings of the 13th International Conference on Automated Planning and Scheduling (ICAPS-03), pp.12-21, 2003.

S. Edelkamp, Planning with pattern databases, Recent Advances in AI Planning. 6th European Conference on Planning (ECP-01), pp.13-24, 2001.

R. Givan, S. M. Leach, and T. Dean, Model reduction techniques for computing approximately optimal solutions for Markov Decision Processes, Artificial Intelligence, vol.122, issue.12, pp.1-8, 1997.

R. Givan, S. M. Leach, and T. Dean, Bounded-parameter Markov decision processes, Artificial Intelligence, vol.122, issue.1-2, pp.71-109, 2000.
DOI : 10.1016/S0004-3702(00)00047-3

P. Haslum, A. Botea, M. Helmert, B. Bonet, and S. Koenig, Domain-independent construction of pattern database heuristics for cost-optimal planning, Proceedings of the 22nd National Conference of the American Association for Artificial Intelligence (AAAI-07), pp.1007-1012, 2007.

M. Helmert, P. Haslum, and J. Hoffmann, Flexible abstraction heuristics for optimal sequential planning, Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS-07), pp.176-183, 2007.

M. Kattenbelt, M. Kwiatkowska, G. Norman, and D. Parker, A game-based abstraction-refinement framework for??Markov decision processes, Formal Methods in System Design, vol.58, issue.1???2, pp.246-280, 2010.
DOI : 10.1007/s10703-010-0097-6

M. Katz, J. Hoffmann, and M. Helmert, How to relax a bisimulation?, Proceedings of the 22nd International Conference on Automated Planning and Scheduling, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00677299

R. Nissim, J. Hoffmann, and M. Helmert, Computing perfect heuristics in polynomial time: On bisimulation and merge-and-shrink abstraction in optimal planning, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), pp.1983-1990, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00592438

R. T. Ortner and S. Singh, Adaptive aggregation for reinforcement learning in average reward Markov Decision Processes Lossy stochastic game abstraction with bounds Abstraction pathologies in extensive games, Proceedings of the 13th ACM Conference on Electronic Commerce Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - AA- MAS '09, pp.880-897, 2009.