activity
20092023
most citedThe Swarmathon: An Autonomous Swarm Robotics Competition

12 citations · 51 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CY2023

Building Resilience to Climate Driven Extreme Events with Computing Innovations: A Convergence Accelerator Report

Elizabeth Bradley, Chandra Krintz, Melanie Moses

In 2022, the National Science Foundation (NSF) funded the Computing Research Association (CRA) to conduct a workshop to frame and scope a potential Convergence Accelerator research…

cs.AI2021★ 4 cited

Frontiers in Collective Intelligence: A Workshop Report

Tyler Millhouse, Melanie Moses, Melanie Mitchell

In August of 2021, the Santa Fe Institute hosted a workshop on collective intelligence as part of its Foundations of Intelligence project. This project seeks to advance the field o…

cs.NE2021

Frontiers in Evolutionary Computation: A Workshop Report

Tyler Millhouse, Melanie Moses, Melanie Mitchell

In July of 2021, the Santa Fe Institute hosted a workshop on evolutionary computation as part of its Foundations of Intelligence in Natural and Artificial Systems project. This pro…

cs.DC2020★ 1 cited

LoCUS: A multi-robot loss-tolerant algorithm for surveying volcanic plumes

John Erickson, Abhinav Aggarwal, G. Matthew Fricke +1

Measurement of volcanic CO2 flux by a drone swarm poses special challenges. Drones must be able to follow gas concentration gradients while tolerating frequent drone loss. We prese…

cs.DC2019

On the Minimal Set of Inputs Required for Efficient Neuro-Evolved Foraging

John Erickson, Abhinav Aggarwal, Melanie E. Moses

In this paper, we perform an ablation study of \neatfa, a neuro-evolved foraging algorithm that has recently been shown to forage efficiently under different resource distributions…

cs.DC2019★ 3 cited

A Most Irrational Foraging Algorithm

Abhinav Aggarwal, William F. Vining, Diksha Gupta +2

We present a foraging algorithm, GoldenFA, in which search direction is chosen based on the Golden Ratio. We show both theoretically and empirically that GoldenFA is more efficient…