Tootfinder

This https://arxiv.org/abs/2309.08322 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet
Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging problem that requires online coordination and information fusion. The majority of existing works either assume a passive all-to-all observation model to minimize the summed uncertainties over all targets by all robots, or optimize over the jointed discr…

Team Coordination on Graphs: Problem, Analysis, and Algorithms
Manshi Limbu, Yanlin Zhou, Gregory Stein, Xuan Wang, Daigo Shishika, Xuesu Xiao
https://arxiv.org/abs/2403.15946

Team Coordination on Graphs: Problem, Analysis, and Algorithms
Team Coordination on Graphs with Risky Edges (TCGRE) is a recently emerged problem, in which a robot team collectively reduces graph traversal cost through support from one robot to another when the latter traverses a risky edge. Resembling the traditional Multi-Agent Path Finding (MAPF) problem, both classical and learning-based methods have been proposed to solve TCGRE, however, they lacked either computation efficiency or optimality assurance. In this paper, we reformulate TCGRE as a constra…

This https://arxiv.org/abs/2309.11190 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Space and Move-optimal Arbitrary Pattern Formation on Infinite Rectangular Grid by Oblivious Robot Swarm
Arbitrary Pattern Formation (APF) is a fundamental coordination problem in swarm robotics. It requires a set of autonomous robots (mobile computing units) to form an arbitrary pattern (given as input) starting from any initial pattern. This problem has been extensively investigated in continuous and discrete scenarios, with this study focusing on the discrete variant. A set of robots is placed on the nodes of an infinite rectangular grid graph embedded in the euclidean plane. The movements of e…

Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections
Muhammad Faris, Mario Zanon, Paolo Falcone
https://arxiv.org/abs/2404.14048

Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections
In this paper, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs). The main objective is to have a safe and optimal crossing order for vehicles approaching unsignalized intersections. This problem results in a mixed-integer quadratic programming (MIQP) formulation which is unsuitable for real-time applications. Therefore, we propose a computationally tractable optimization-based heuristic that monitors plato…

Resource Slicing with Cross-Cell Coordination in Satellite-Terrestrial Integrated Networks
Mingcheng He (Sherman), Huaqing Wu (Sherman), Conghao Zhou (Sherman), Xuemin (Sherman), Shen
https://arxiv.org/abs/2404.13158

Resource Slicing with Cross-Cell Coordination in Satellite-Terrestrial Integrated Networks
Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we propose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimi…

Reputation in Repeated Global Games of Regime Change with Exit
Daniel Luo
https://arxiv.org/abs/2404.18884 https://arxiv.org/pdf/2404…

Reputation in Repeated Global Games of Regime Change with Exit
I study a repeated binary-action supermodular game with endogenous exit where many short-lived agents attempt to coordinate a revolt against a regime. The regime undertakes costly actions to increase the short-run players' coordination frictions, though acts only after if the revolt is unsuccessful, inducing a lack-of-commitment problem. In the complete-information repeated game, a folk theorem holds, with payoff multiplicity arising due to both the regime's dynamic incentives and agents' stage…

This https://arxiv.org/abs/2301.10550 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Structural insulators and promotors in networks under generic problem-solving dynamics
The collective coordination of distributed tasks in a complex system can be represented as decision dynamics on a graph. This abstract representation allows studying the performance of local decision heuristics as a function of task complexity and network architecture. Here we identify hard-to-solve and easy-to-solve networks in a social differentiation task within the basic model of small-world graphs. We show that, depending on the details of the decision heuristic as well as the length of th…

This https://arxiv.org/abs/2301.04090 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems
Networked discrete dynamical systems are often used to model the spread of contagions and decision-making by agents in coordination games. Fixed points of such dynamical systems represent configurations to which the system converges. In the dissemination of undesirable contagions (such as rumors and misinformation), convergence to fixed points with a small number of affected nodes is a desirable goal. Motivated by such considerations, we formulate a novel optimization problem of finding a nontr…

Scaling Team Coordination on Graphs with Reinforcement Learning
Manshi Limbu, Zechen Hu, Xuan Wang, Daigo Shishika, Xuesu Xiao
https://arxiv.org/abs/2403.05787

Scaling Team Coordination on Graphs with Reinforcement Learning
This paper studies Reinforcement Learning (RL) techniques to enable team coordination behaviors in graph environments with support actions among teammates to reduce the costs of traversing certain risky edges in a centralized manner. While classical approaches can solve this non-standard multi-agent path planning problem by converting the original Environment Graph (EG) into a Joint State Graph (JSG) to implicitly incorporate the support actions, those methods do not scale well to large graphs …

Open Ad Hoc Teamwork with Cooperative Game Theory
Jianhong Wang, Yang Li, Yuan Zhang, Wei Pan, Samuel Kaski
https://arxiv.org/abs/2402.15259 https://

Open Ad Hoc Teamwork with Cooperative Game Theory
Ad hoc teamwork poses a challenging problem, requiring the design of an agent to collaborate with teammates without prior coordination or joint training. Open ad hoc teamwork further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams. The state-of-the-art solution to this problem is graph-based policy learning (GPL), leveraging the generalizability of graph neural networks to handle an unrestricted number of agents and effective…

Scaling Team Coordination on Graphs with Reinforcement Learning
Manshi Limbu, Zechen Hu, Xuan Wang, Daigo Shishika, Xuesu Xiao
https://arxiv.org/abs/2403.05787

Scaling Team Coordination on Graphs with Reinforcement Learning
This paper studies Reinforcement Learning (RL) techniques to enable team coordination behaviors in graph environments with support actions among teammates to reduce the costs of traversing certain risky edges in a centralized manner. While classical approaches can solve this non-standard multi-agent path planning problem by converting the original Environment Graph (EG) into a Joint State Graph (JSG) to implicitly incorporate the support actions, those methods do not scale well to large graphs …

Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution
Yidong Bai, Toshiharu Sugawara
https://arxiv.org/abs/2404.13096

Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution
In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the redundant computation problem. This occurs when multiple agents redundantly perform the same or similar computation due to overlapping observations. To address this issue, this study introduces a novel method referred to as locally centralized team transformer (LCTT). LCTT establishes a locally centralized execution framework where selected agents serve as leaders, issuing instructions, …

This https://arxiv.org/abs/2311.16916 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stein Variational Belief Propagation for Multi-Robot Coordination
Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. We apply SVBP to multi-robot coordination b…

A general mechanism for enhancer-insulator pairing reveals heterogeneous dynamics in long-distant 3D gene regulation
Lucas Hedstr\"om, Ralf Metzler, Ludvig Lizana
https://arxiv.org/abs/2402.09209

A general mechanism for enhancer-insulator pairing reveals heterogeneous dynamics in long-distant 3D gene regulation
Cells regulate fates and complex body plans using spatiotemporal signaling cascades that alter gene expression. Enhancers, short DNA sequences (50-150 base pairs), help coordinate these cascades by attracting regulatory proteins to enhance the transcription of distal genes by binding to promoters. In humans, there are hundreds of thousands of enhancers dispersed across the genome, which poses a challenging coordination task to prevent unintended gene activation. To mitigate this problem, the ge…

This https://arxiv.org/abs/2307.04004 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction
Next-Best View (NBV) planning is a long-standing problem of determining where to obtain the next best view of an object from, by a robot that is viewing the object. There are a number of methods for choosing NBV based on the observed part of the object. In this paper, we investigate how predicting the unobserved part helps with the efficiency of reconstructing the object. We present, Multi-Agent Prediction-Guided NBV (MAP-NBV), a decentralized coordination algorithm for active 3D reconstruction…