Monte-Carlo Tree Search for the Physical Travelling Salesman Problem

  • Diego Perez
  • Philipp Rohlfshagen
  • Simon M. Lucas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

The significant success of MCTS in recent years, particularly in the game Go, has led to the application of MCTS to numerous other domains. In an ongoing effort to better understand the performance of MCTS in open-ended real-time video games, we apply MCTS to the Physical Travelling Salesman Problem (PTSP). We discuss different approaches to tailor MCTS to this particular problem domain and subsequently identify and attempt to overcome some of the apparent shortcomings. Results show that suitable heuristics can boost the performance of MCTS significantly in this domain. However, visualisations of the search indicate that MCTS is currently seeking solutions in a rather greedy manner, and coercing it to balance short term and long term constraints for the PTSP remains an open problem.

Keywords

Monte Carlo Video Game Domain Knowledge Travelling Salesman Problem Game Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Diego Perez
    • 1
  • Philipp Rohlfshagen
    • 1
  • Simon M. Lucas
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUnited Kingdom

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