Definition
Speedup learning is a branch of machine learning that studies learning mechanisms for speeding up problem solvers based on problem-solving experience. The input to a speedup learner typically consists of observations of prior problem-solving experience, which may include traces of the problem solver’s operations and/or solutions to solve the problems. The output is knowledge that the problem solver can exploit to find solutions more quickly than before learning without seriously effecting the solution quality. The most distinctive feature of speedup learning, compared with most branches of machine learning, is that the learned knowledge does not provide the problem solver with the ability to solve new problem instances. Rather, the learned knowledge is intended solely to facilitate faster solution times compared to the solver without the knowledge.
Motivation and Background
Much of the work in computer science and especially artificial intelligence aims at developing...
Recommended Reading
Beame, P., Kautz, H., & Sabharwal, A. (2004). Towards understanding and harnessing the potential of clause learning. Journal of Artificial Intelligence Research, 22, 319–351.
Boyan, J. A., & Moore, A. W. (1998). Learning evaluation functions for global optimization and boolean satisfiability. In National conference on artificial intelligence (pp. 3–10). Mlenio Park, CA: AAAI Press.
Fikes, R., Hart, P., & Nilsson, N. (1972). Learning and executing generalized robot plans. Artificial Intelligence, 3(1–3), 251–288.
Huang, Y.-C., Selman, B., & Kautz, H. (2000). Learning declarative control rules for constraint-based planning. In International conference on machine learning (pp. 415–422). San Francisco: Morgan Kaufmann.
Kambhampati, S. (1998). On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning. Artificial Intelligence, 105(1-2), 161–208.
Khardon, R. (1999). Learning action strategies for planning domains. Artificial Intelligence, 113(1-2), 125–148.
Kumar, V., & Lin, Y. (1988). A data-dependency based intelligent backtracking scheme for prolog. The Journal of Logic Programming, 5(2), 165–181.
Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In National conference on artificial intelligence (pp. 564–569). St. Paul, MN: Morgan Kaufmann.
Minton, S. (Ed.) (1993). Machine learning methods for planning. San Francisco: Morgan Kaufmann.
Minton, S., Carbonell, J., Knoblock, C. A., Kuokka, D. R., Etzioni, O., & Gil, Y. (1989). Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40, 63–118.
Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 211–229.
Sarkar, S., Chakrabarti, P., & Ghose, S. (1998). Learning whiles solving problems in best first search. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans, 28(4), 553–541.
Schiex, T., & Verfaillie, G. (1994). Nogood recording for static and dynamic constraint satisfaction problems. International Journal on Artificial Intelligence Tools, 3(2), 187–207.
Tadepalli, P., & Natarajan, B. (1996). A formal framework for speedup learning from problems and solutions. Journal of Artificial Intelligence Research, 4, 445–475.
Zimmerman, T., & Kambhampati, S. (2003). Learning-assisted automated planning: Looking back, taking stock, going forward. AI Magazine, 24(2), 73–96.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
Fern, A. (2011). Speedup Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_772
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_772
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering