Solving Belief-Driven Pathfinding using Monte-Carlo Tree Search

Authors

  • Davide Aversa
  • Stavros Vassos

DOI:

https://doi.org/10.26503/dl.v2016i2.898

Keywords:

pathfinding, artificial intelligence, beliefs, monte carlo ts

Abstract

In this work we discuss a stochastic extension to the (discrete) Belief-Driven Pathfinding (BDP) approach for finding personalized paths based on the beliefs of a character about the current state of the map. Our stochastic BDP upgrades previous work to the more realistic setting of using probabilities for the beliefs and takes advantage of approximate Monte Carlo Tree Search approaches.

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Published

2016-01-01

Bibtex

@Conference{digra898, title ="Solving Belief-Driven Pathfinding using Monte-Carlo Tree Search", year = "2016", author = "Aversa, Davide and Vassos, Stavros", publisher = "DiGRA", address = "Tampere", howpublished = "\url{https://doi.org/10.26503/dl.v2016i2.898}", booktitle = "Abstract Proceedings of DiGRA/FDG 2016 Conference"}

Proceedings

2016: Abstract Proceedings of DiGRA/FDG 2016 Conference

Section

Papers

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