Abstract
We propose a new approach for search tree exploration in the context of combinatorial optimization, specifically Mixed Integer Programming (MIP), that is based on UCT, an algorithm for the multi-armed bandit problem designed for balancing exploration and exploitation in an online fashion. UCT has recently been highly successful in game tree search. We discuss the differences that arise when UCT is applied to search trees as opposed to bandits or game trees, and provide initial results demonstrating that the performance of even a highly optimized state-of-the-art MIP solver such as CPLEX can be boosted using UCT’s guidance on a range of problem instances.
A preliminary version of this paper appeared at the Workshop on Monte-Carlo Tree Search held in Freiburg, Germany in June 2011. The current implementation relies on a newer version of the CPLEX solver, capitalizing on additional cuts learned during search and resulting in significantly improved performance.
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References
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2-3), 235–256 (2002)
Ciancarini, P., Favini, G.P.: Monte Carlo tree search techniques in the game of Kriegspiel. In: 21st IJCAI, Pasadena, CA, pp. 474–479 (July 2009)
Finnsson, H., Björnsson, Y.: Simulation-based approach to general game playing. In: 23rd AAAI, Chicago, IL, pp. 259–264 (July 2008)
Gelly, S., Silver, D.: Combining online and offline knowledge in UCT. In: 24th ICML, Corvallis, OR, pp. 273–280 (June 2007)
Gelly, S., Silver, D.: Achieving master level play in 9 ×9 computer Go. In: 23rd AAAI, Chicago, IL, pp. 1537–1540 (July 2008)
IBM ILOG. IBM CPLEX Optimization Studio 12.3 (2011)
Kocsis, L., Szepesvári, C.: Bandit Based Monte-Carlo Planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006)
Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization. Wiley-Interscience (1999)
Previti, A., Ramanujan, R., Schaerf, M., Selman, B.: Applying UCT to Boolean Satisfiability. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 373–374. Springer, Heidelberg (2011)
Ramanujan, R., Sabharwal, A., Selman, B.: Understanding sampling style adversarial search methods. In: 26th UAI, Catalina Island, CA (July 2010)
Ramanujan, R., Selman, B.: Trade-offs in sampling-based adversarial planning. In: 21st ICAPS, Freiburg, Germany (June 2011)
Wolsey, L.A.: Integer Programming. Wiley-Interscience (1998)
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Sabharwal, A., Samulowitz, H., Reddy, C. (2012). Guiding Combinatorial Optimization with UCT. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds) Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems. CPAIOR 2012. Lecture Notes in Computer Science, vol 7298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29828-8_23
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DOI: https://doi.org/10.1007/978-3-642-29828-8_23
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