| Title: |
Reinforcement Learnign and Automated Planning: A Survey |
| Author(s): |
I. Partalas, D. Vrakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file.
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| Keywords: |
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| Appeared in: |
Artificial Intelligence for Advanced Problem Solving Techniques, D. Vrakas and I. Vlahavas (Eds.), IGI, pp. 148-165, 2008.
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| Abstract: |
This chapter presents a detailed survey on Artificial Intelligent approaches that combine Reinforcement Learning and Automated Planning. There is a close relationship between those two areas, as they both deal with the process of guiding an agent, situated in a dynamic environment, in order to achieve a set of predefined goals. Therefore, it is straightforward to integrate learning and planning in a single guiding mechanism and there have been many approaches in this direction during the past years. The approaches are organized and presented according to various characteristics, as the used planning mechanism or the reinforcement learning algorithm. |
| See also : |
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This paper has been cited by the following:
| 1 |
B. Al-Khateeb, G. Kendall, "Introducing a Round Robin Tournament into Blondie24", 2009 IEEE Symposium on Computational Intelligence and Games, pp. 112-116, 2009 |
| 2 |
Zhang, W. , Shen, L. , Chen, J., "Learning and fatigue inspired method for optimized HTN planning", Journal of Systems Engineering and Electronics, Volume 23, Issue 2, pp. 233-241, 2012
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| 3 |
J. Feliu, "USE OF REINFORCEMENT LEARNING (RL) FOR PLAN GENERATION IN BELIEF-DESIRE-INTENTION (BDI) AGENT SYSTEMS", Master Thesis, University of Rhode Island, 2013. |
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