Biologically Inspired Neural Path Finding
This work addresses pathfinding in generalized graphs, offering a biologically inspired approach that is incremental in nature.
The paper tackles the problem of finding optimal low-cost paths in graphs by developing a biologically inspired computational framework that can handle unseen graphs at test time and adapt to node additions or removals while maintaining fixed prediction time.
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