Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Helen Harman, Elizabeth I. Sklar
Multi-agent task allocation methods seek to distribute a set of tasks fairly amongst a set of agents. In real-world settings, such as soft fruit farms, human labourers undertake harvesting tasks. The harvesting workforce is typically organised by farm manager(s) who assign workers to the fields that are ready to be…
Niklas Dahlquist, Shridhar Velhal, George Nikolakopoulos
This paper presents a scalable framework for multi-robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction-based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable…
Jing Liu, Fangfei Li, Xin Jin, Yang Tang
Optimization Approach Authors: ['Jing Liu' 'Fangfei Li' 'Xin Jin' 'Yang Tang'] Abstract— This paper investigates dynamic task allocation for multi-agent systems (MASs) under resource constraints, with a focus on maximizing the global utility of agents while ensuring a conflict-free allocation of targets. We present a…
Martin Braquet, Efstathios Bakolas
We propose a decentralized auction-based algorithm for the solution of dynamic task allocation problems for spatially distributed multi-agent systems. In our approach, each member of the multi-agent team is assigned to at most one task from a set of spatially distributed tasks, while several agents can be allocated to…
Niklas Dahlquist, Björn Lindqvist, Akshit Saradagi, George Nikolakopoulos
'George Nikolakopoulos'] Abstract— This article presents an architecture for multiagent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as…
Niall Creech, Natalia Criado, Simon Miles
In large-scale systems there are fundamental challenges when centralised techniques are used for task allocation. The number of interactions is limited by resource constraints such as on computation, storage, and network communication. We can increase scalability by implementing the system as a distributed…
Omer Melih Gul, Sebastian Bader
In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are $(M+1)$ robots in the cluster and M tasks exist in each round. In the cluster, a…
WeiWei Du, XiaoWei Chen
As task diversity and inter-task relationship complexity grow, optimal formation power allocation is key to improving task execution efficiency. This paper proposes a task optimization allocation method with multiple groups collaboration, constructing a task network based on analysis of task static characteristics and…
Mohammad Reza Daneshvaramoli, Mohammad Sina Kiarostami, Saleh Khalaj Monfared, Helia Karisani + 4 more
'Saleh Khalaj Monfared' 'Helia Karisani' 'Hamed Khashehchi' 'Dara Rahmati' 'Saeid Gorgin' 'Amir Rahmati'] Abstract. Cooperative task assignment is an important subject in multiagent systems with a wide range of applications. These systems are usually designed with massive communication among the agents to minimize the…
Nayyer Fazal, Muhammad Tahir Khan, Shahzad Anwar, Javaid Iqbal + 2 more
'Shahbaz Khan' 'Mohd Nadhir Ab Wahab'] Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to…
Vivian Cremer Kalempa, Luis Piardi, Marcelo Limeira, André Schneider de Oliveira + 3 more
'André Schneider de Oliveira' 'Hilde Perez' 'Javier Díez-González' 'Rubén Álvarez'] This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as…
Yang Xu, Xiang Li, Ming Liu
With the expansion of distributed multiagent systems, traditional coordination strategy becomes a severe bottleneck when the system scales up to hundreds of agents. The key challenge is that in typical large multiagent systems, sparsely distributed agents can only communicate directly with very few others and the…
Rui Chen, Bernd Meyer, Julian García
Social insect colonies are capable of allocating their workforce in a decentralised fashion; addressing a variety of tasks and responding effectively to changes in the environment. This process is fundamental to their ecological success, but the mechanisms behind it remain poorly understood. While most models focus on…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Anshuman Swain, Sara D. Williams, Louisa J. Di Felice, Elizabeth A. Hobson
In animal societies, individuals may take on different roles to fulfil their own needs and the needs of their groups. Ant colonies display high levels of organisational complexity, with ants fulfilling different roles at different timescales (what is known as task allocation). Factors affecting task allocation can be…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Momchil S. Tomov, Eric Schulz, Samuel J. Gershman
The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions.…
Augustin Chartouny, Mehdi Khamassi, Benoît Girard
Unlike humans, who continuously adapt to both known and unknown situations, most reinforcement learning agents struggle to adjust to changing environments. In this paper, we present a new model-based reinforcement learning method that adapts to local task changes online. This method can detect changes at the level of…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Nicholas Franklin, Michael J. Frank
Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers. Often, this entails generalizing constituent pieces of experiences that do not fully overlap, but nonetheless share useful similarities with, previously acquired knowledge. However, it is often…