Spiking Nonlinear Opinion Dynamics (S-NOD) for Agile Decision-Making

TL;DR

This paper introduces S-NOD, a novel two-dimensional excitable model for decision-making that enhances agility and adaptability in dynamic environments, demonstrated through multi-robot navigation.

Contribution

The paper presents the first model of excitable dynamics for decision-making, extending NOD with a new term inspired by excitable systems to improve agility.

Findings

S-NOD exhibits fast, flexible, and adaptive decision responses.

The model is validated in multi-robot navigation scenarios.

S-NOD outperforms previous models in dynamic environments.

Abstract

We present, analyze, and illustrate a first-of-its-kind model of two-dimensional excitable (spiking) dynamics for decision-making over two options. The model, Spiking Nonlinear Opinion Dynamics (S-NOD), provides superior agility, characterized by fast, flexible, and adaptive response to rapid and unpredictable changes in context, environment, or information received about available options. S-NOD derives through the introduction of a single extra term to the previously presented Nonlinear Opinion Dynamics (NOD) for fast and flexible multi-agent decision-making behavior. The extra term is inspired by the fast-positive, slow-negative mixed-feedback structure of excitable systems. The agile behaviors brought about by the new excitable nature of decision-making driven by S-NOD are analyzed in a general setting and illustrated in an application to multi-robot navigation around human movers.