Skip to main content
Article thumbnail
Location of Repository

Monte-Carlo Planning for Pathfinding in Real-Time Strategy Games

By Munir Naveed, Diane E. Kitchin and Andrew Crampton


In this work, we explore two Monte-Carlo planning approaches: Upper Confidence Tree (UCT) and Rapidlyexploring Random Tree (RRT). These Monte-Carlo planning approaches are applied in a real-time strategy game for solving the path finding problem. The planners are evaluated using a grid-based representation of our game world. The results show that the UCT planner solves the path planning problem with significantly less search effort than the RRT planner. The game playing performance of each planner is evaluated using the mean, maximum and minimum scores in the test games. With respect to the mean scores, the RRT planner shows better performance than the UCT planner. The RRT planner achieves more maximum scores than the UCT planner in the test games

Topics: QA, QA75
Publisher: PlanSIG
Year: 2010
OAI identifier:

Suggested articles


  1. (2010). A Novel Transition Based Encoding Scheme for Planning as Satisfiability.
  2. (1997). An OpenEnded Finite Domain Constraint Solver. Programming Languages: Implementations, Logics, and Programs.
  3. (2004). Automated Planning: Theory and Practice.
  4. (1995). Complexity results for SAS+ planning.
  5. (2008). Fast Planning by Search in Domain Transition Graphs.
  6. (1997). Fast planning through planning graph analysis.
  7. (2009). Forward Constraint-Based Algorithms for Anytime Planning.
  8. (2006). Introduction to the Theory of Computation, Second Edition, Thomson Course Technology. Author Index
  9. (2010). Solving Sequential Planning Problems via Constraint Satisfaction. Fundamenta Informaticae, Volume 99, Number 2,
  10. (2006). The Fast Downward Planning System.
  11. (1989). Theory of finite automata: with an introduction to formal languages.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.