Croonenborghs, T., Ramon, J., Blockeel, H. and Bruynooghe, M. (2006) Model-Assisted Approaches for Relational Reinforcement Learning: Some Challenges for the SRL Community. Proceedings of the ICML-2006 Work-shop on Open Problems in Statistical Relational Learning, Pittsburgh.
 Fernandez, F. and Veloso, M. (2006) Probabilistic Policy Reuse in a Reinforcement Learning Agent. Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems, New York, 720-727. http://dx.doi.org/10.1145/1160633.1160762
 Kitakoshi, D., Shioya, H. and Nakano, R. (2004) Adaptation of the Online Policy-Improving System by Using a Mixture Model of Bayesian Networks to Dynamic Environments. Electronics, Information and Communication Engineers, 104, 15-20.
 Kitakoshi, D., Shioya, H. and Nakano, R. (2010) Empirical Analysis of an On-Line Adaptive System Using a Mixture of Bayesian Networks. Information Science, 180, 2856-2874. http://dx.doi.org/10.1016/j.ins.2010.04.001
 Phommasak, U., Kitakoshi, D. and Shioya, H. (2012) An Adaptation System in Unknown Environments Using a Mix- ture Probability Model and Clustering Distributions. Journal of Advanced Computational Intelligence and Intelligent Informatics, 16, 733-740.
 Tanaka, F. and Yamamura, M. (1997) An Approach to Lifelong Reinforcement Learning through Multiple Environments. Proceedings of the Sixth European Workshop on Learning Robots, EWLR-6, Brighton, 93-99.
 Minato, T. and Asada, M. (1998) Environmental Change Adaptation for Mobile Robot Navigation. Proceedings of IEEE/RSJ International Joint Conference on Intelligent Robots and Systems, IROS’98, Victoria, 1859-1864.