---
title: "Monte-Carlo Exploration for Deterministic Planning"
authors:
  - "Hootan Nakhost"
  - "Martin Müller"
doi: ""
url: "https://ijcai.org/proceedings/2009/294"
pdf: "https://ijcai.org/proceedings/2009/0294.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "1766-1770"
track: ""
keywords:
---

# Monte-Carlo Exploration for Deterministic Planning

Hootan Nakhost, Martin Müller

*Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence*, pages 1766-1770.

## Abstract

Search methods based on Monte-Carlo simulation have recently led to breakthrough performance improvements in difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk (MRW) planning applies Monte-Carlo ideas to deterministic classical planning. In the forward chaining planner Arvand, Monte-Carlo random walks are used to explore the local neighborhood of a search state for action selection. In contrast to the stochastic local search approach used in the recent planner Identidem, random walks yield a larger and unbiased sample of the search neighborhood, and require state evaluations only at the endpoints of each walk. On IPC-4 competition problems, the performance of Arvand is competitive with state of the art systems.Hootan Nakhost, Martin Müller