Biologically Inspired Neural Path Finding

TL;DR

This paper introduces a biologically inspired neural framework for dynamic, low-cost pathfinding in graphs that adapts to unseen structures and node modifications while maintaining constant prediction time.

Contribution

It presents a novel neural approach inspired by brain functions to efficiently find and adapt to optimal paths in generalized graphs, handling unseen and changing graph topologies.

Findings

Capable of handling unseen graphs at test time

Finds alternate optimal paths with node modifications

Maintains fixed prediction time despite graph changes

Abstract

The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths in case some neurons are damaged. Moreover, the brain is capable of retaining information and applying it to similar but completely unseen scenarios. In this paper, we take inspiration from these attributes of the brain, to develop a computational framework to find the optimal low cost path between a source node and a destination node in a generalized graph. We show that our framework is capable of handling unseen graphs at test time. Moreover, it can find alternate optimal paths, when nodes are arbitrarily added or removed during inference, while maintaining a fixed prediction time. Code is available here: https://github.com/hangligit/pathfinding