We investigate the application of a multi-objective genetic algorithm to the problem of task allocation in a self-organizing, decentralized, threshold-based swarm. Each agent in our system is capable of performing four tasks with a response threshold for each, and we seek to assign response threshold values to all of the agents a swarm such that the collective behavior of the swarm is optimized. Random assignment of threshold values according to a uniform distribution is known to be effective; however, this method does not consider features of particular problem instances. Dynamic response thresholds have some flexibility to address problem specific features through real-time adaptivity, often improving swarm performance.
In this work, we use a multi-objective genetic algorithm to evolve response thresholds for a simulated swarm engaged in a dynamic task allocation problem: two-dimensional collective tracking. We show that evolved thresholds not only outperform uniformly distributed thresholds and dynamic thresholds but achieve nearly optimal performance on a variety of tracking problem instances (target paths). More importantly, we demonstrate that thresholds evolved for one of several problem instances generalize to all other problem instances eliminating the need to evolve new thresholds for each problem to be solved. We analyze the properties that allow these paths to serve as universal training instances and show that they are quite natural.
Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involve…
We present a novel reinforcement learning (RL) based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. Our approach is designed for scenarios in which multiple robots are used to perform various pick up and delivery tasks. We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it. At the higher level, we solve the task allocation by formulating it in terms of Markov Decision Processes and choosing the appropriate rewards to minimize the Total Travel Delay (TTD). At the lower level…
Bar-Ilan University（巴伊兰大学）；Nanyang Technological University（南洋理工大学）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）
We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network. In the considered network, the impoverished channel sensing/probing capability and computational resource on the IoT devices make them difficult to acquire the detailed Channel State Information (CSI) for the shared multiple channels. In practice, the unknown patterns of the primary users' transmission activities and the time-varying CSI (e.g., due to small-scale fading or device mobility) also cause stochastic changes in the channel qu…
We propose a decentralized game-theoretic framework for dynamic task allocation problems for multi-agent systems. In our problem formulation, the agents' utilities depend on both the rewards and the costs associated with the successful completion of the tasks assigned to them. The rewards reflect how likely is for the agents to accomplish their assigned tasks whereas the costs reflect the effort needed to complete these tasks (this effort is determined by the solution of corresponding optimal control problems). The task allocation problem considered herein corresponds to a dynamic game whose s…
We consider the general problem of geometric task allocation, wherein a large, decentralised swarm of simple mobile agents must detect the locations of tasks in the plane and position themselves nearby. The tasks are represented by an a priori unknown demand profile $Φ(x,y)$ that determines how many agents are needed in each location. The agents are autonomous, oblivious and indistinguishable, and have finite sensing range. They must configure themselves according to $Φ$ using only local information about $Φ$ and about the positions of nearby agents. All agents act according to the same local …
We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states or partial states are observable. The task allocation module then aims at maximizing the expected pure reward (reward minus cost) of the robotic team. We fuse the Markov model into a factor graph f…
In this paper, we develop a class of decentralized algorithms for solving a convex resource allocation problem in a network of $n$ agents, where the agent objectives are decoupled while the resource constraints are coupled. The agents communicate over a connected undirected graph, and they want to collaboratively determine a solution to the overall network problem, while each agent only communicates with its neighbors. We first study the connection between the decentralized resource allocation problem and the decentralized consensus optimization problem. Then, using a class of algorithms for s…
Indian Institute of Science（印度科学学院）；Nanyang Technological University（南洋理工大学）
This paper provides a new solution approach to a multi-player perimeter defense game, in which the intruders' team tries to enter the territory, and a team of defenders protects the territory by capturing intruders on the perimeter of the territory. The objective of the defenders is to detect and capture the intruders before the intruders enter the territory. Each defender independently senses the intruder and computes his trajectory to capture the assigned intruders in a cooperative fashion. The intruder is estimated to reach a specific location on the perimeter at a specific time. Each intru…
Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions for coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously id…
XAI-Guided Conservative Decentralized Execution for Offline Multi-Agent Network Slicing
Centre for Wireless Communications (CWC), University of Oulu, Finland（奥卢大学无线通信中心）；i2CAT Foundation, Spain（西班牙i2CAT基金会）；Research Institute for Digital Future, Khalifa University, 127788 Abu Dhabi, UAE（哈利法大学数字未来研究所）
A swarm of quadcopters can perform cooperative tasks, such as monitoring of a large area, more efficiently than a single one. However, to be able to successfully work together, the quadcopters must be aware of the position of the other swarm members, especially to avoid collisions. A quadcopter can share its own position by transmitting it via radio waves and in order to allow multiple quadcopters to communicate effectively, a decentralized channel access protocol is essential. We propose a new dynamic channel access protocol, called Dynamic time slot allocation (DTSA), where the quadcopters s…
For efficiency reasons, manycore systems are increasingly heterogeneous, which makes the mapping of complex workloads a key problem with a high optimization potential. Constraints express the application requirements like which core type to choose, how many cores to choose, exclusively or non-exclusively, using a certain core, etc. In this work, we propose a decentralized solution for solving application resource constraints by means of an agent-based approach in order to obtain scalability. We translate the constraints into a Distributed Constraint Optimization Problem (DCOP) and propose a lo…
Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation
Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST)（卡奥斯特大学科学与技术学院电气与计算机工程系）；Department of Electrical Engineering, Federal University of Sergipe (UFS)（塞尔希培联邦大学电气工程系）
We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing between competition and cooperation. The mechanism provably has Nash equilibria with optimal resource allocation in the static case. For a dynamic environment, we propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information, and a reward signal that reduces information need to a great extent. Empirical results confirm that through le…
University of Science and Technology of China（中国科学技术大学）；Huawei Cloud（华为云）
Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has become one of the most widely used MARL paradigms. In CTDE, centralized information is dedicated to learning the allocation of the team reward with a mixing network, while the learning of individual Q-values is usually based on local observations. The insufficient utility of global observation will degrade performance in challenging environments. To this end, this work proposes a novel Centralized Teacher with Decen…
University of Michigan（密歇根大学）；Stevens Institute of Technology（史蒂文斯理工学院）；US Army CCDC Ground Vehicle Systems Center（美国陆军作战能力发展司令部地面车辆系统中心）
In operations of multi-agent teams ranging from homogeneous robot swarms to heterogeneous human-autonomy teams, unexpected events might occur. While efficiency of operation for multi-agent task allocation problems is the primary objective, it is essential that the decision-making framework is intelligent enough to manage unexpected task load with limited resources. Otherwise, operation effectiveness would drastically plummet with overloaded agents facing unforeseen risks. In this work, we present a decision-making framework for multi-agent teams to learn task allocation with the consideration …
