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IEEE International Conference on Systems, Man, and Cybernetics

作者： Itshak Tkach Yael Edan Aleksandar Jevti? Shimon Y. Nof Dept. of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva Israel Robosoft Bidart France School of Industrial Engineering Purdue University West Lafayette USA

ISBN: (纸本)9781479906505

In this paper, we propose a Modified distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probabilistic approach to maximize information gain while minimizing costs. tasks are allocated based on sensors' performance, tasks' priorities and the mutual sensor-task distances. Simulation analysis compared different algorithms and indicated improved performance of 15% by using MDBA with respect to the second-best algorithm.

关键词： multi-agent systems distributed task allocation swarm intelligence

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distributed Strategy Adaptation with a Prediction Function in Multi-Agent task allocation 18

Distributed Strategy Adaptation with a Prediction Function i...

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Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

作者： Joanna Turner Qinggang Meng Gerald Schaefer Andrea Soltoggio Loughborough University Loughborough United Kingdom

Coordinating multiple agents to complete a set of tasks under time constraints is a complex problem. distributed consensus-based task allocation algorithms address this problem without the need for human supervision. With such algorithms, agents add tasks to their own schedule according to specified allocation strategies. Various factors, such as the available resources and number of tasks, may affect the efficiency of a particular allocation strategy. The novel idea we suggest is that each individual agent can predict locally the best task inclusion strategy, based on the limited task assignment information communicated among networked agents. Using supervised classification learning, a function is trained to predict the most appropriate strategy between two well known insertion heuristics. Using the proposed method, agents are shown to correctly predict and select the optimal insertion heuristic to achieve the overall highest number of task allocations. The adaptive agents consistently match the performances of the best non-adaptive agents across a variety of scenarios. This study aims to demonstrate the possibility and potential performance benefits of giving agents greater decision making capabilities to independently adapt the task allocation process in line with the problem of interest.

关键词： multi-agent systems scheduling distributed task allocation

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distributed task Management in Cyber-Physical Systems: How to Cooperate Under Uncertainty?

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2019年第1期5卷 165-180页

作者： Maghsudi, Setareh van der Schaar, Mihaela Tech Univ Berlin Dept Elect Engn & Comp Sci D-10623 Berlin Germany Univ Oxford Dept Engn Sci Oxford OX1 2JD England Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

We consider the problem of task allocation in a network of cyber-physical systems (CPSs). The network can have different states, and the tasks are of different types. The task arrival is stochastic and state-dependent. Every CPS is capable of performing each type of task with some specific state-dependent efficiency. The CPSs have to agree on task allocation prior to knowing about the realized network's state and/or the arrived tasks. We model the problem as a multi-state stochastic cooperative game with state uncertainty. We then use the concept of deterministic equivalence and sequential core to solve the problem. We establish the non-emptiness of the strong sequential core in our designed task allocation game and investigate its characteristics including uniqueness and optimality. Moreover, we prove that in the task allocation game, the strong sequential core is equivalent to Walrasian equilibrium under state uncertainty;consequently, it can be implemented by using the Walras' tatonnement process.

关键词： Cyber-physical systems distributed task allocation stochastic cooperative games uncertainty

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Reliable, distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2018年第2期15卷 732-747页

作者： Whitbrook, Amanda Meng, Qinggang Chung, Paul W. H. Univ Derby Dept Elect Comp & Math Derby DE22 1GB England Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England

This paper addresses two main problems with many heuristic task allocation approaches-solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensus-based bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm's exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners-This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for

关键词： Adaptive systems auction-based scheduling distributed task allocation multiagent systems

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A distributed task reassignment method in dynamic environment for multi-UAV system

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APPLIED INTELLIGENCE 2022年第2期52卷 1582-1601页

作者： Yang, Mi Bi, Wenhao Zhang, An Gao, Fei Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

This paper considers the task reassignment problem for distributed multiple Unmanned Aerial Vehicle (multi-UAV) systems in dynamic environment. For a dynamic reassignment problem in a multi-UAV system, the task information may be subject to different dynamic events, and many existing task allocation algorithms require much computation and communication resource to achieve a feasible solution. Hence, this paper proposes a distributed method to cope with dynamic events that occur online during the execution of original schedules. First, a distributed framework for determining the processing strategy according to the types of dynamic events is introduced. Second, a partial reassignment algorithm (PRA) is proposed to support the framework and an incremental subteam formation mechanism and a partial releasing mechanism are developed to release the computation and communication burden. Furthermore, a modified inclusion phase to maximize assignment (MIP-MA) is also proposed in PRA to maximize the number of task allocations. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution with less data exchanges and runtime.

关键词： Multi-UAV system Dynamic environment distributed task allocation Heuristic algorithm

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distributed Service-Based Cooperation in Aerial/Ground Robot Teams Applied to Fire Detection and Extinguishing Missions

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ADVANCED ROBOTICS 2010年第1-2期24卷 1-23页

作者： Viguria, Antidio Maza, Ivan Ollero, Anibal Ctr Adv Aerosp Technol CATEC Seville 41309 Spain Univ Seville Robot Vis & Control Grp Seville 41092 Spain

This paper presents a system for the coordination of aerial and ground robots for applications such as surveillance and intervention in emergency management. The overall system architecture is described. An important part for the coordination between robots is the task allocation strategy. A distributed market-based algorithm, called S+T, has been developed to solve the multi-robot task allocation problem in applications that require cooperation among the robots to accomplish all the tasks. Using this algorithm, robots can provide transport and communication relay services dynamically to other robots during the missions. Moreover, the paper presents a demonstration with a team of heterogeneous robots (aerial and ground) cooperating in a mission of fire detection and extinguishing. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2010

关键词： distributed task allocation market-based coordination auctions multi-robot heterogeneous teams

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distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target allocation in Swarms of Robots

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SENSORS 2011年第11期11卷 10880-10893页

作者： Jevtic, Aleksandar Gutierrez, Alvaro Univ Politecn Madrid ETSI Telecomunicac E-28040 Madrid Spain

Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.

关键词： swarm robotics multi-agent systems cooperative sensors distributed task allocation parameter optimization genetic algorithms

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Market Clearing-based Dynamic Multi-agent task allocation

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2020年第1期11卷 4-4页

作者： Nelke, Sofia Amador Okamoto, Steven Zivan, Roie HIT 52 Golomb StPOB 305 IL-5810201 Holon Israel Google 805 N Highland Ave Pittsburgh PA 15206 USA Ben Gurion Univ Negev POB 653 IL-8410501 Beer Sheva Israel

Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents. However, when such problems include temporal and spatial constraints that require tasks to be executed sequentially by agents, they are NP-hard, and thus are commonly solved using general and specifically designed incomplete heuristic algorithms. We propose FMC_TA, a novel such incomplete task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions. FMC_TA first finds allocations that are fair (envy-free), balancing the load and sharing important tasks among agents, and efficient (Pareto optimal) in a simplified version of the problem. It computes such allocations in polynomial or pseudo-polynomial time (centrally or distributedly, respectively) using a Fisher market with agents as buyers and tasks as goods. It then heuristically schedules the allocations, taking into account inter-agent constraints on shared tasks. We empirically compare our algorithm to state-of-the-art incomplete methods, both centralized and distributed, on law enforcement problems inspired by real police logs. We present a novel formalization of the law enforcement problem, which we use to perform our empirical study. The results show a clear advantage for FMC_TA in total utility and in measures in which law enforcement authorities measure their own performance. Besides problems with realistic properties, the algorithms were compared on synthetic problems in which we increased the size of different elements of the problem to investigate the algorithm's behavior when the problem scales. The domination of the proposed algorithm was found to be consistent.

关键词： distributed task allocation Multi agent system

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task-Extended Utility Tensor Method for Decentralized Multi-Vehicle Mission Planning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 6081-6093页

作者： Tian, Haoyu Yao, Weiran Dong, Bo Wang, Xiashuang Sun, Guanghui Wu, Ligang Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China China Aerosp Sci & Ind Corp CASIC Intelligent Sci & Technol Acad Ltd Beijing 100043 Peoples R China China Aerosp Sci & Ind Corp CASIC Acad 2 Beijing 100039 Peoples R China

In multi-vehicle systems, the coupling problem between the task allocation and path planning and the variability of task execution solutions creates challenges for utility estimation and affects the effectiveness of distributed mission planning. To characterize the effect of task sequences on the task utilities and implement a task-extended distributed allocation, we propose a task-extended utility tensor algorithm (TEUTA) based on market mechanism. In the mission planning problem of multi-vehicle system, we consider the impact of the task schedule on the vehicle trajectory, and indicate the vehicle task execution utilities under different preceding task points in the form of tensors. Further, a task-extended utility tensor iterative algorithm (TEUTIA) is presented based on an iterative strategy to improve the algorithm in terms of computational complexity. A task execution utility estimation model and an algorithm framework are designed for the implementation of the two proposed algorithms. The simulation and experimental results show that compared with the non-tensor method, TEUTA and TEUTIA can achieve higher task execution performance, and TEUTIA has better computational efficiency.

关键词： Multi-vehicle systems distributed task allocation task-extended tensor mission planning iterative strategy

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How Good Are distributed allocation Algorithms for Solving Urban Search and Rescue Problems? A Comparative Study With Centralized Algorithms

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2019年第1期16卷 478-485页

作者： Geng, Na Meng, Qinggang Gong, Dunwei Chung, Paul W. H. Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO.

关键词： Centralized task allocation distributed task allocation particle swarm optimization (PSO) search and rescue

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