Exploring mazes at random
Nikita Gladkov, Igor Pak

TL;DR
This paper analyzes a probabilistic depth-first search algorithm on mazes with two exits, demonstrating that it has an equal chance of finding either exit through a combinatorial proof.
Contribution
It introduces a probabilistic variant of depth-first search for mazes and provides a combinatorial proof of its symmetry in exit discovery.
Findings
The algorithm has equal probability of finding either exit.
A combinatorial involution proves the symmetry property.
The approach offers insights into probabilistic maze exploration.
Abstract
We consider a probabilistic version of the depth-first search on mazes with two exits, and show that this algorithm has equal probability of finding either exit. The proof is combinatorial and uses an explicit involution.
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Machine Learning and Data Classification · Data Mining Algorithms and Applications
