A Navigation Algorithm Inspired by Human Navigation

TL;DR

This paper introduces a novel network path-finding algorithm inspired by human navigation strategies, particularly landmark recognition and top-ranked node learning, demonstrating comparable path properties to human experiments.

Contribution

The paper presents a new navigation algorithm based on human landmark recognition, simulating human-like path-finding in networks, and compares its performance with existing methods.

Findings

Paths from the simulation match properties of human navigation paths

The method effectively identifies key network nodes for navigation

Performance comparison shows advantages over traditional algorithms

Abstract

Human navigation has been a topic of interest in spatial cognition from the past few decades. It has been experimentally observed that humans accomplish the task of way-finding a destination in an unknown environment by recognizing landmarks. Investigations using network analytic techniques reveal that humans, when asked to way-find their destination, learn the top ranked nodes of a network. In this paper we report a study simulating the strategy used by humans to recognize the centers of a network. We show that the paths obtained from our simulation has the same properties as the paths obtained in human based experiment. The simulation thus performed leads to a novel way of path-finding in a network. We discuss the performance of our method and compare it with the existing techniques to find a path between a pair of nodes in a network.