| Title: |
Learning Rules for Adaptive Planning |
| Author(s): |
D. Vrakas, G. Tsoumakas, N. Bassiliades, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (10 pages).
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| Keywords: |
planning and learning, domain-independent classical planning, machine learning, knowledge based systems.
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| Appeared in: |
Proc. 13th International Conference on Automated Planning and Scheduling (ICAPS '03), pp. 82-91, Trento, Italy, June 2003, 2003.
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| Abstract: |
This paper presents a novel idea, which combines Planning, Machine Learning and Knowledge-Based techniques. It is concerned with the development of an adaptive planning system that can fine-tune its planning parameters based on the values of specific measurable characteristics of the given planning problem. Adaptation is guided by a rule-based system, whose knowledge has been acquired through machine learning techniques. Specifically, the algorithm of classification based on association rules was applied to a large dataset produced by results from experiments on a large number of problems used in the three AIPS Planning competitions. The paper presents experimental results with the adaptive planner, which demonstrate the boost in performance of the planning system. |
| See also : |
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This paper has been cited by the following:
| 1 |
D. Borrajo, M. Veloso, R. Aller, S. Fernandez, “Incremental and Non-incremental Learning of Control Knowledge for Planning”, Symposium on Reasoning and Learning in Cognitive Systems, Stanford University, 2004. |
| 2 |
D. P. Benjamin, D. Lonsdale, D. Lyons, “Integrating Perception, Language and Problem Solving in a Cognitive Agent for a Mobile Robot”, 3rd International Joint Conference on Intelligent Agents and Multiagent Systems, NY, 2004. |
| 3 |
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| 4 |
D. Borrajo, M. Veloso, “Planning and Learning”, 14th International Conference on Automated Planning and Scheduling, Tutorial, Whistler Canada, 2004 |
| 5 |
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Proyectos Software", Proc. 5th ADIS 2004 Workshop on Decision Support in
Software Engineering. Malaga (Spain). Nov., 2004. |
| 6 |
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| 7 |
Frank, J. “Using data mining to enhance automated planning and
scheduling”, Proc. 2007 IEEE Symposium on Computational Intelligence and
Data Mining, CIDM 2007, pp. 251-260. |
| 8 |
M. Roberts, A. Howe, L. Flom, “Learned Models of Performance for Many
Planners”, Proc. ICAPS 07 Workshop on Artificial Intelligence Planning
and Learning. |
| 9 |
A. Gerevini, A. Saetti and M. Vallati, "Learning and Exploiting Configuration Knowledge for a Portfolio-based Planner", ICAPS-09 Workshop on Planning and Learning, Thessaloniki, Greece, September 20th, 2009 |
| 10 |
Bc. Tomáš Kučečka, “Choosing planning approach based on problem domain
analyses”, MSc Thesis, Faculty Of Informatics And Information
Technologies, Slovak University of Technology Bratislava, May, 2010. |
| 11 |
Mauro Vallati, "Pianificazione Automatica Efficiente Mediante Configurazione Automatica di Algoritmi e Machine Learning", PhD Thesis, Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Brescia, 2011 |
| 12 |
Mauro Vallati, Chris Fawcett, Alfonso E. Gerevini, Holger H. Hoos, Alessandro Saetti, “Generating Fast Domain-Specific Planners by Automatically Configuring a Generic Parameterised Planner”, 3rd Workshop On Learning And Planning, 21st International Conference on Automated Planning and Scheduling (ICAPS-11), Freiburg, Germany, June 11-16, 2011. |
| 13 |
Jendrik Seipp, Manuel Braun, Johannes Garimort and Malte Helmert, “Learning Portfolios of Automatically Tuned Planners”, In Proceedings of the 22nd International Conference on Automated Planning and Scheduling (ICAPS 2012). 2012. |
| 14 |
L. Kotthoff, "Algorithm selection for combinatorial search problems: A survey," Oct. 2012. [Online]. Available: http://arxiv.org/abs/1210.7959 |
| 15 |
Maher Alhossaini, J. Christopher Beck, “Instance-Specific Remodelling of Planning Domains by Adding Macros and Removing Operators”, Proceedings of the Tenth Symposium on Abstraction, Reformulation, and Approximation, Leavenworth, Washington, 11-12 July 2013, AAAI Press, pp. 16-24, 2013. |
| 16 |
Maher Alhossaini, “Remodeling Planning Domains Using Macro Operators and Machine Learning”, PhD thesis, Department of Computer Science, University of Toronto, 2013. |
| 17 |
Jean Lucas de Sousa, Carlos Roberto Lopes. Aplicando tecnicas de aprendizado de maquina em planejamento probabilistico. XI Encontro Nacional de Inteligência Artificial e Computacional, 2014. |
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