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Добірка наукової літератури з теми "Multi-Task agent"

Автор: Grafiati

Опубліковано: 21 грудня 2024

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Статті в журналах з теми "Multi-Task agent"

Wu, Xiaohu, Yihao Liu, Xueyan Tang, Wentong Cai, Funing Bai, Gilbert Khonstantine, and Guopeng Zhao. "Multi-Agent Pickup and Delivery with Task Deadlines." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 21, 2021): 206–8. http://dx.doi.org/10.1609/socs.v12i1.18585.

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We study the multi-agent pickup and delivery problem with task deadlines, where a team of agents execute tasks with individual deadlines to maximize the number of tasks completed by their deadlines. We take an integrated approach that assigns and plans one task at a time taking into account the agent states resulting from all the previous task assignments and path planning. We define metrics to effectively determine which agent ought to execute a given task and which task is most worth assignment next. We leverage the bounding technique to greatly improve the computational efficiency.

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Surynek, Pavel. "Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 21, 2021): 197–99. http://dx.doi.org/10.1609/socs.v12i1.18582.

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Xie, Bing, Xueqiang Gu, Jing Chen, and LinCheng Shen. "A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems." International Journal of Advanced Robotic Systems 15, no. 6 (November 1, 2018): 172988141881303. http://dx.doi.org/10.1177/1729881418813037.

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In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

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Pei, Zhaoyi, Songhao Piao, Meixiang Quan, Muhammad Zuhair Qadir, and Guo Li. "Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142092021. http://dx.doi.org/10.1177/1729881420920216.

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This article proposes a unique active relative localization mechanism for multi-agent simultaneous localization and mapping, in which an agent to be observed is considered as a task, and the others who want to assist that agent will perform that task by relative observation. A task allocation algorithm based on deep reinforcement learning is proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent simultaneous localization and mapping on its own initiative. By this way, the process of each agent simultaneous localization and mapping will be interacted by the collaboration. Firstly, a unique observation function which models the whole multi-agent system is obtained based on ORBSLAM. Secondly, a novel type of Deep Q Network called multi-agent systemDeep Q Network (MAS-DQN) is deployed to learn correspondence between Q value and state–action pair, abstract representation of agents in multi-agent system is learned in the process of collaboration among agents. Finally, each agent must act with a certain degree of freedom according to MAS-DQN. The simulation results of comparative experiments prove that this mechanism improves the efficiency of cooperation in the process of multi-agent simultaneous localization and mapping.

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Thiele, Veikko. "Task-specific abilities in multi-task principal–agent relationships." Labour Economics 17, no. 4 (August 2010): 690–98. http://dx.doi.org/10.1016/j.labeco.2009.12.003.

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Nedelmann, Déborah Conforto, Jérôme Lacan, and Caroline P. C. Chanel. "SKATE : Successive Rank-based Task Assignment for Proactive Online Planning." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 396–404. http://dx.doi.org/10.1609/icaps.v34i1.31499.

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Анотація:

The development of online applications for services such as package delivery, crowdsourcing, or taxi dispatching has caught the attention of the research community to the domain of online multi-agent multi-task allocation. In online service applications, tasks (or requests) to be performed arrive over time and need to be dynamically assigned to agents. Such planning problems are challenging because: (i) few or almost no information about future tasks is available for long-term reasoning; (ii) agent number, as well as, task number can be impressively high; and (iii) an efficient solution has to be reached in a limited amount of time. In this paper, we propose SKATE, a successive rank-based task assignment algorithm for online multi-agent planning. SKATE can be seen as a meta-heuristic approach which successively assigns a task to the best-ranked agent until all tasks have been assigned. We assessed the complexity of SKATE and showed it is cubic in the number of agents and tasks. To investigate how multi-agent multi-task assignment algorithms perform under a high number of agents and tasks, we compare three multi-task assignment methods in synthetic and real data benchmark environments: Integer Linear Programming (ILP), Genetic Algorithm (GA), and SKATE. In addition, a proactive approach is nested to all methods to determine near-future available agents (resources) using a receding-horizon. Based on the results obtained, we can argue that the classical ILP offers the better quality solutions when treating a low number of agents and tasks, i.e. low load despite the receding-horizon size, while it struggles to respect the time constraint for high load. SKATE performs better than the other methods in high load conditions, and even better when a variable receding-horizon is used.

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Rodiah, Iis, Medria Kusuma Dewi Hardhienata, Agus Buono, and Karlisa Priandana. "Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 6 (December 27, 2022): 911–22. http://dx.doi.org/10.29207/resti.v6i6.4201.

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Анотація:

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.

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Wang, Yijuan, Weijun Pan, and Kaiyuan Liu. "Multi-Agent Aviation Search Task Allocation Method." IOP Conference Series: Materials Science and Engineering 646 (October 17, 2019): 012058. http://dx.doi.org/10.1088/1757-899x/646/1/012058.

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Pal, Anshika, Ritu Tiwari, and Anupam Shukla. "Communication constraints multi-agent territory exploration task." Applied Intelligence 38, no. 3 (September 15, 2012): 357–83. http://dx.doi.org/10.1007/s10489-012-0376-6.

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Surynek, Pavel. "Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12409–17. http://dx.doi.org/10.1609/aaai.v35i14.17472.

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Анотація:

We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We suggest two novel algorithms using different paradigms to address MG-MAPF: a heuristic search-based algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the satisfiability modulo theories (SMT), called SMT-Hamiltonian-CBS (SMT-HCBS).

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Більше джерел

Дисертації з теми "Multi-Task agent"

Macarthur, Kathryn. "Multi-agent coordination for dynamic decentralised task allocation." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/209737/.

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Анотація:

Coordination of multiple agents for dynamic task allocation is an important and challenging problem, which involves deciding how to assign a set of agents to a set of tasks, both of which may change over time (i.e., it is a dynamic environment). Moreover, it is often necessary for heterogeneous agents to form teams to complete certain tasks in the environment. In these teams, agents can often complete tasks more efficiently or accurately, as a result of their synergistic abilities. In this thesis we view these dynamic task allocation problems as a multi-agent system and investigate coordination techniques for such systems. In more detail, we focus specially on the distributed constraint optimisation problem (DCOP) formalism as our coordination technique. Now, a DCOP consists of agents, variables and functions agents must work together to find the optimal configuration of variable values. Given its ubiquity, a number of decentralised algorithms for solving such problems exist, including DPOP, ADOPT, and the GDL family of algorithms. In this thesis, we examine the anatomy of the above-mentioned DCOP algorithms and highlight their shortcomings with regard to their application to dynamic task allocation scenarios. We then explain why the max-sum algorithm (a member of the GDL family) is the most appropriate for our setting, and define specific requirements for performing multi-agent coordination in a dynamic task allocation scenario: namely, scalability, robustness, efficiency in communication, adaptiveness, solution quality, and boundedness. In particular, we present three dynamic task allocation algorithms: fast-max-sum, branchand-bound fast-max-sum and bounded fast-max-sum, which build on the basic max-sum algorithm. The former introduces storage and decision rules at each agent to reduce overheads incurred by re-running the algorithm every time the environment changes. However, the overall computational complexity of fast-max-sum is exponential in the number of agents that could complete a task in the environment. Hence, in branchand- bound fast-max-sum, we give fast-max-sum significant new capabilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents, at the expense of additional storage. Despite this, fast-max-sum is only proven to converge to an optimal solution on instances where the underlying graph contains no cycles. In contrast, bounded fast-max-sum builds on techniques found in bounded max-sum, another extension of max-sum, to find bounded approximate solutions on arbitrary graphs. Given such a graph, bounded fast-max-sum will run our iGHS algorithm, which computes a maximum spanning tree on subsections of a graph, in order to reduce overheads when there is a change in the environment. Bounded fast-max-sum will then run fast-max-sum on this maximum spanning tree in order to find a solution. We have found that fast-max-sum reduces the size of messages communicated and the amount of computation by up to 99% compared with the original max-sum. We also found that, even in large environments, branch-and-bound fast-max-sum finds a solution using 99% less computation and up to 58% fewer messages than fast-max-sum. Finally, we found bounded fast-max-sum reduces the communication and computation cost of bounded max-sum by up to 99%, while obtaining 60{88% of the optimal utility, at the expense of needing additional communication than using fast-max-sum alone. Thus, fast-max-sum or branch-and-bound fast-max-sum should be used where communication is expensive and provable solution quality is not necessary, and bounded fast-max-sum where communication is less expensive, and provable solution quality is required. Now, in order to achieve such improvements over max-sum, fast-max-sum exploits a particularly expressive model of the environment by modelling tasks in the environment as function nodes in a factor graph, which need to have some communication and computation performed for them. An equivalent problem to this can be found in operations research, and is known as scheduling jobs on unrelated parallel machines (also known as RjjCmax). In this thesis, we draw parallels between unrelated parallel machine scheduling and the computation distribution problem, and, in so doing, we present the spanning tree decentralised task distribution algorithm (ST-DTDA), the first decentralised solution to RjjCmax. Empirical evaluation of a number of heuristics for ST-DTDA shows solution quality achieved is up to 90% of the optimal on sparse graphs, in the best case, whilst worst-case quality bounds can be estimated within 5% of the solution found, in the best case

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Turner, Joanna. "Distributed task allocation optimisation techniques in multi-agent systems." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36202.

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Анотація:

A multi-agent system consists of a number of agents, which may include software agents, robots, or even humans, in some application environment. Multi-robot systems are increasingly being employed to complete jobs and missions in various fields including search and rescue, space and underwater exploration, support in healthcare facilities, surveillance and target tracking, product manufacturing, pick-up and delivery, and logistics. Multi-agent task allocation is a complex problem compounded by various constraints such as deadlines, agent capabilities, and communication delays. In high-stake real-time environments, such as rescue missions, it is difficult to predict in advance what the requirements of the mission will be, what resources will be available, and how to optimally employ such resources. Yet, a fast response and speedy execution are critical to the outcome. This thesis proposes distributed optimisation techniques to tackle the following questions: how to maximise the number of assigned tasks in time restricted environments with limited resources; how to reach consensus on an execution plan across many agents, within a reasonable time-frame; and how to maintain robustness and optimality when factors change, e.g. the number of agents changes. Three novel approaches are proposed to address each of these questions. A novel algorithm is proposed to reassign tasks and free resources that allow the completion of more tasks. The introduction of a rank-based system for conflict resolution is shown to reduce the time for the agents to reach consensus while maintaining equal number of allocations. Finally, this thesis proposes an adaptive data-driven algorithm to learn optimal strategies from experience in different scenarios, and to enable individual agents to adapt their strategy during execution. A simulated rescue scenario is used to demonstrate the performance of the proposed methods compared with existing baseline methods.

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Kivelevitch, Elad H. "Robust, Real Time, and Scalable Multi-Agent Task Allocation." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337007279.

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Suárez, Barón Silvia Andrea. "Dynamic task allocation and coordination in cooperative multi-agent environments." Doctoral thesis, Universitat de Girona, 2011. http://hdl.handle.net/10803/7754.

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Анотація:

La coordinació i assignació de tasques en entorns distribuïts ha estat un punt important de la recerca en els últims anys i aquests temes són el cor dels sistemes multi-agent. Els agents en aquests sistemes necessiten cooperar i considerar els altres agents en les seves accions i decisions. A més a més, els agents han de coordinar-se ells mateixos per complir tasques complexes que necessiten més d'un agent per ser complerta. Aquestes tasques poden ser tan complexes que els agents poden no saber la ubicació de les tasques o el temps que resta abans de que les tasques quedin obsoletes. Els agents poden necessitar utilitzar la comunicació amb l'objectiu de conèixer la tasca en l'entorn, en cas contrari, poden perdre molt de temps per trobar la tasca dins de l'escenari. De forma similar, el procés de presa de decisions distribuït pot ser encara més complexa si l'entorn és dinàmic, amb incertesa i en temps real. En aquesta dissertació, considerem entorns amb sistemes multi-agent amb restriccions i cooperatius (dinàmics, amb incertesa i en temps real). En aquest sentit es proposen dues aproximacions que permeten la coordinació dels agents. La primera és un mecanisme semi-centralitzat basat en tècniques de subhastes combinatòries i la idea principal es minimitzar el cost de les tasques assignades des de l'agent central cap als equips d'agents. Aquest algoritme té en compte les preferències dels agents sobre les tasques. Aquestes preferències estan incloses en el bid enviat per l'agent. La segona és un aproximació d'scheduling totalment descentralitzat. Això permet als agents assignar les seves tasques tenint en compte les preferències temporals sobre les tasques dels agents. En aquest cas, el rendiment del sistema no només depèn de la maximització o del criteri d'optimització, sinó que també depèn de la capacitat dels agents per adaptar les seves assignacions eficientment. Addicionalment, en un entorn dinàmic, els errors d'execució poden succeir a qualsevol pla degut a la incertesa i error de accions individuals. A més, una part indispensable d'un sistema de planificació és la capacitat de re-planificar. Aquesta dissertació també proveeix una aproximació amb re-planificació amb l'objectiu de permetre als agent re-coordinar els seus plans quan els problemes en l'entorn no permeti la execució del pla. Totes aquestes aproximacions s'han portat a terme per permetre als agents assignar i coordinar de forma eficient totes les tasques complexes en un entorn multi-agent cooperatiu, dinàmic i amb incertesa. Totes aquestes aproximacions han demostrat la seva eficiència en experiments duts a terme en l'entorn de simulació RoboCup Rescue.
Distributed task allocation and coordination have been the focus of recent research in last years and these topics are the heart of multi-agent systems. Agents in these systems need to cooperate and consider the other agents in their actions and decisions. Moreover, agents may have to coordinate themselves to accomplish complex tasks that need more than one agent to be accomplished. These tasks may be so complicated that the agents may not know the location of them or the time they have before the tasks become obsolete. Agents may need to use communication in order to know the tasks in the environment, otherwise, it may take a long time to find the tasks into the scenario. Similarly, the distributed decisionmaking process may be even more complex if the environment is dynamic, uncertain and real-time. In this dissertation, we consider constrained cooperative multi-agent environments (dynamic, uncertain and real-time). In this regard, we propose two approaches that enable the agents to coordinate themselves. The first one is a semi-centralized mechanism based on combinatorial auction techniques and the main idea is minimizing the cost of assigned tasks from the central agent to the agent teams. This algorithm takes into account the tasks' preferences of the agents. These preferences are included into the bid sent by the agent. The second one is a completely decentralized scheduling approach. It permits agents schedule their tasks taking into account temporal tasks' preferences of the agents. In this case, the system's performance depends not only on the maximization or the optimization criterion, but also on the agents' capacity to adapt their schedule efficiently. Furthermore, in a dynamic environment, execution errors may happen to any plan due to uncertainty and failure of individual actions. Therefore, an indispensable part of a planning system is the capability of replanning. This dissertation is also providing a replanning approach in order to allow agents recoordinate his plans when the environmental problems avoid fulfil them. All these approaches have been carried out to enable the agents to efficiently allocate and coordinate all their complex tasks in a cooperative, dynamic and uncertain multi-agent scenario. All these approaches have demonstrated their effectiveness in experiments performed in the RoboCup Rescue simulation environment.

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KARAMI, HOSSEIN. "Task planning and allocation for multi-agent collaborative robot systems." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1083925.

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Анотація:

In this thesis we address the problem of human robot interaction in industrial environments from collaboration perspective. This thesis, in particular, focuses on introducing novel frameworks for coordination of heterogeneous teams made of humans and robots that collaboratively aim to reach to a common goal. In the last decade, robots has received enormous attentions for being employed in both industrial environments and workplaces. Thin is mainly because of a number of reasons: (I) the shift of mass production industries toward autonomous industry units, (II) huge amount of financial and scientific investments on robotics, and (III) substitution of humans with robots to accomplish hazardous and stressful tasks. However Due to the limited cognitive knowledge and reasoning of the robots in accomplishing complex operations, they still are not able to operate in a fully autonomous fashion and independently from their human counterparts. Therefore presence of human operators, as a complementary counterparts, in workplaces becomes fundamental for robots to become utterly safe, reliable and operative. The goal of this thesis is to design and implement a framework whereby humans and/or robots can together play a complementary role, while applying their individual skills to accomplish a task. Human-robot collaboration (HRC) is defined as the purposeful interaction among humans and robots in a shared space, and it is aimed at a common goal. The design of such framework for HRC problems, requires to satisfy many requirements from which flexibility, adaptability and safety, are the primary characteristics of such framework. In this thesis we mainly focus on multi-agent robot systems task allocation and planning. We consider two main aspects in defining our objectives: on one hand we investigate on HRC, and implement alternative frameworks to model and study collaborations in industrial scenarios considering various roles of humans in coordination and collaboration with robots. On the other hand, the presence of humans is neglected and it is assumed that robots are able to fully precept the environment independently from human cognitive support, as this can be the case of future where Artificial intelligence might substitute the skills of humans. To model a HRC scenario, a smart framework is required to start, coordinate and terminate a collaborative process. This framework, in particular has to be aware of agents and their types, determine their responsibilities and roles, be aware of their physical structure, define the logical relationship among the agent and handle the collaboration process fluently. In this thesis, to address the framework described above, we propose different frameworks and evaluate their effectiveness in solving HRC problems. To formulate task planning and allocation problem , we introduce and implement three variants of AND/OR graphs, namely, c-layer AND/OR graphs, Branched AND/OR graphs, and Iteratively deepened AND/OR graphs. The first two variants aim at addressing the problem of task allocation among humans and collaborative robots in object defect inspection (ODI) scenarios in HRC context. Instead, the third variant targets Task and motion planning (TAMP) problems for heterogeneous robots. TAMP problems, compared to HRC problem, is not only responsible for allocating task among agents at higher-level, but also at lower level, it plans motions for agents and ultimately, interconnects higher levels of task planning to lower levels of motion planning and control to achieve a complete planning framework. To validate the applicability and scalability of our proposed frameworks, we design and implement various real-world and simulation experiments and we also evaluate their effectiveness in terms of achieving desired objectives, and quantitatively with other available methods in the literature.

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Basran, Jagdeep S. "Agent-based management of a task-level multi-robot assembly cell." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0022/NQ32437.pdf.

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Day, Michael. "Multi-Agent Task Negotiation Among UAVs to Defend Against Swarm Attacks." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/6784.

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Анотація:

This research involves a multi-agent based simulation modeling a large swarm of adversarial UAVs attacking a surface target and groups of friendly UAVs responding to thwart the attack. Defense systems need to cooperatively negotiate which enemy systems to engage to maximize the number of aggressor systems destroyed. Using optimal centralized task assignment methods as a baseline, various distributed methods are examined for efficiency and effectiveness. Our findings indicate that the optimality of distributed methods does approach that of centralized methods, though further study is warranted in future simulations with additional constraints, and in field experimentation with physical UAVs. We further find that the number of defender agents, the effectiveness of their weapon systems, and their speeds contribute significantly to the defender swarm’s effectiveness.

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Al-Karkhi, A. "Task recovery in self-organised multi-agent systems for distributed domains." Thesis, University of Essex, 2018. http://repository.essex.ac.uk/22816/.

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Анотація:

Grid computing and cloud systems are distributed systems which provide substantial widely-accessible services to resources. Quality of service is affected by the issues around resource allocation, sharing, task execution and node failure. The focus of this research is on task execution in distributed environments and the effects of node failure on service provision. Most methods in the literature which provide fault tolerance, use reactive techniques; these provide solutions to failure only after its occurrence. In contrast, this research argues that using multi-agent systems with self-organising capabilities can provide a proactive methodology which can improve task execution in open, dynamic and distributed environments. We have modelled a system of autonomous agents with heterogeneous resources and proposed a new delegation protocol for executing tasks within their time constraints. This helps avoid the loss of tasks and to improve efficiency. However, this method on its own is not sufficient in terms of task execution throughput, especially in the presence of agent failure. Hence, we propose, a self-organisation technique. This is represented in this research by two different mechanisms for creating organisations of agents with a certain structure; we suggest, in addition, the adoption of task delegation within the organisations. Adding an organisation structure with agent roles to the network enables smoother performance, increases task execution throughput and copes with agent failures. In addition, we study the failure problem as it manifests within the organisations and we suggest an improvement to the organisation structure which involves the use of another protocol and adding a new role. An exploratory study of dynamic, heterogeneous organisations of agents has also been conducted to understand the formation of organisations in a dynamic environment where agents may fail and new agents may join organisations. These conditions mean that new organisations may evolve and existing organisations may change.

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Ahmadoun, Douae. "Interdependent task allocation via coalition formation for cooperative multi-agent systems." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7088.

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L'allocation des tâches à plusieurs agents autonomes devant accomplir des tâches complexes a été l'un des domaines de recherche récents sur les systèmes multi-agents. Dans de nombreuses applications, les agents sont coopératifs et doivent effectuer des tâches qui nécessitent chacune une combinaison de différentes capacités dont peut se doter un sous-ensemble d'agents. Dans ce cas, nous pouvons utiliser la formation de coalitions comme paradigme pour affecter des coalitions d'agents à des tâches. Les solutions à ce problème d'allocation de tâches, pour les systèmes robotiques en particulier, trouvent plusieurs applications dans le monde réel et prennent de plus en plus de l'importance dans les domaines de la défense, de l'espace, de la gestion des catastrophes, de l'exploration sous-marine, de la logistique, de la fabrication de produits et de l'assistance dans les services de santé. De multiples mécanismes de formation de coalitions et d'allocation de tâches ont été introduits dans l'état de l'art, tenant rarement compte des tâches interdépendantes. Cependant, il est récurrent de trouver des tâches dont la qualité ne peut être évaluée sans considérer les autres tâches dans des applications réelles. Ces tâches sont appelées interdépendantes par opposition aux tâches indépendantes qui, elles, peuvent être évaluées individuellement, ce qui entraîne une évaluation globale de l'allocation des tâches qui additionne simplement toutes les évaluations des tâches. La recherche dans le passé a conduit à de nombreuses méthodes d'allocation de tâches qui traitent le cas des tâches indépendantes sous différents angles et sous différents paradigmes. D'autres travaux résolvent le cas des tâches interdépendantes, mais ils le font soit de manière centralisée avec une complexité très élevée, soit uniquement pour le cas des dépendances de précédence. Cependant, de nombreuses formes d'interdépendance peuvent exister entre les tâches dans les applications du monde réel. Ces applications nécessitent que les mécanismes d'allocation des tâches soient décentralisés et anytime, pouvant renvoyer une solution à tout moment quitte à l'améliorer s'il reste du temps, pour répondre à des problèmes de sensibilité au temps et de robustesse. Dans cette thèse, nous considérons des environnements multi-agents coopératifs où les tâches sont multi-agents et interdépendantes, et les méthodes d'allocation des tâches doivent être décentralisées et anytime. À cet égard, nous proposons une formalisation du problème qui considère les attributs qualitatifs et quantitatifs des agents et des tâches, et qui capture les dépendances des tâches que ça soit au niveau des exigences ou au niveau de l'évaluation des allocations. Nous introduisons une nouvelle approche avec un mécanisme de formation de coalition décentralisé anytime qui permet aux agents dotés de capacités complémentaires de former, de manière autonome et dynamique, des structures de coalitions faisables qui accomplissent une tâche globale et composite. Cette approche est basée sur la formation d'une structure de coalition faisable permettant aux agents de décider quelle coalition rejoindre et donc quelle tâche accomplir afin que toutes les tâches soient faisables. Ensuite, les structures formées sont progressivement améliorées via des remplacements d'agents pour optimiser l'évaluation globale de l'allocation, le but étant d'accomplir les tâches avec les meilleures performances possibles. Nous analysons la complexité de nos algorithmes et montrons que, bien que le problème général soit NP-complet, notre mécanisme fournit une solution dans un temps acceptable. Des scénarios d'application simulés sont utilisés pour démontrer la valeur ajoutée de notre approche
Task allocation among multiple autonomous agents that must accomplish complex tasks has been one of the focusing areas of recent research in multi-agent systems. In many applications, the agents are cooperative and have to perform tasks that each requires a combination of different capabilities that a subset of agents can have. In this case, we can use coalition formation as a paradigm to assign coalitions of agents to tasks. For robotic systems, in particular, solutions to this task allocation problem have several and increasingly important real-world applications in defense, space, disaster management, underwater exploration, logistics, product manufacturing, and support in healthcare facilities support. Multiple coalition formation and task allocation mechanisms were introduced in the prior art, seldom accounting for interdependent tasks. However, it is recurrent to find tasks whose quality cannot be evaluated without considering the other tasks in real-world applications. These tasks are called interdependent in contrast to independent tasks that can be individually assessed, resulting in a global evaluation of the tasks' allocation that sums all the tasks' evaluations. Research in the past has led to many task allocation algorithms that address the case of independent tasks from different angles and under different paradigms. Other works solve the case of the interdependent tasks, but they do it either centrally with very high complexity or only for the case of precedence dependencies. However, many forms of interdependence may exist between tasks in real-world applications. In addition, these applications need task allocation mechanisms to be decentralised and available at anytime to allow them to return a solution at any time and to improve it if there is time left, to respond to their time-sensitivity and robustness issues. In this dissertation, we consider cooperative multi-agent environments where tasks are multi-agent and interdependent, and task allocation methods have to be decentralized and available at anytime. In this regard, we propose a problem formalisation that considers the agents' and the tasks' qualitative and quantitative attributes and captures the tasks' dependencies on the requirements level and the allocation evaluation level. We introduce a novel approach with a token-passing anytime decentralised coalition formation mechanism. The approach enables agents with complementary capabilities to form, autonomously and dynamically, feasible coalition structures that accomplish a global, composite task. It is based on forming a feasible coalition structure that allows the agents to decide which coalition to join and thus which task to do so that all the tasks can be feasible. Then, the formed structures are incrementally improved via agent replacements to optimise the global evaluation. The purpose is to accomplish the tasks with the best possible performance. The analysis of our algorithms' complexity shows that although the general problem is NP-complete, our mechanism provides a solution within an acceptable time. Simulated application scenarios are used to demonstrate the added value of our approach

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Tompkins, Mark F. (Mark Freeman) 1979. "Optimization techniques for task allocation and scheduling in distributed multi-agent operations." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16974.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 105-107).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
This thesis examines scenarios where multiple autonomous agents collaborate in order to accomplish a global objective. In the environment that we consider, there is a network of agents, each of which offers different sets of capabilities or services that can be used to perform various tasks. In this environment, an agent formulates a problem, divides it into a precedence-constrained set of sub-problems, and determines the optimal allocation of these sub-problems/tasks to other agents so that they are completed in the shortest amount of time. The resulting schedule is constrained by the execution delay of each service, job release times and precedence relations, as well as communication delays between agents. A Mixed Integer-Linear Programming (MILP) approach is presented in the context of a multi-agent problem-solving framework that enables optimal makespans to be computed for complex classifications of scheduling problems that take many different parameters into account. While the algorithm presented takes exponential time to solve and thus is only feasible to use for limited numbers of agents and jobs, it provides a flexible alternative to existing heuristics that model only limited sets of parameters, or settle for approximations of the optimal solution. Complexity results of the algorithm are studied for various scenarios and inputs, as well as recursive applications of the algorithm for hierarchical decompositions of large problems, and optimization of multiple objective functions using Multiple Objective Linear Programming (MOLP) techniques.
by Mark F. Tompkins.
M.Eng.

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Книги з теми "Multi-Task agent"

Sinclair-Desgagne, Bernard. The first-order approach to multi-task principal-agent problems. Fontainebleau: INSEAD, 1991.

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Kanakia, Anshul. Response Threshold Based Task Allocation in Multi-Agent Systems: Performing Concurrent Benefit Tasks with Limited Information. Nikolaus Correll dba Magellan Scientific, 2016.

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Частини книг з теми "Multi-Task agent"

Tkach, Itshak, and Yael Edan. "Multi-agent Task Allocation." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 9–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_2.

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Singh, Arambam James, Poulami Dalapati, and Animesh Dutta. "Multi Agent Based Dynamic Task Allocation." In Advances in Intelligent Systems and Computing, 171–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07650-8_18.

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Faigl, Jan, Olivier Simonin, and Francois Charpillet. "Comparison of Task-Allocation Algorithms in Frontier-Based Multi-robot Exploration." In Multi-Agent Systems, 101–10. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17130-2_7.

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Strens, Malcolm, and Neil Windelinckx. "Combining Planning with Reinforcement Learning for Multi-robot Task Allocation." In Adaptive Agents and Multi-Agent Systems II, 260–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32274-0_17.

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Chan, Chi-Kong, and Ho-Fung Leung. "Multi-auction Approach for Solving Task Allocation Problem." In Multi-Agent Systems for Society, 240–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03339-1_20.

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Suzuki, Takahiro, and Masahide Horita. "Multi-agent Task Allocation Under Unrestricted Environments." In Group Decision and Negotiation: Methodological and Practical Issues, 31–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07996-2_3.

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Karishma and Shrisha Rao. "Cooperative Task Execution in Multi-agent Systems." In Lecture Notes in Computer Science, 134–47. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70415-4_12.

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Zabłocki, Michał. "Multi-agent Processes Analysis System in Prediction Task." In Advances in Intelligent Systems and Computing, 73–84. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15147-2_7.

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M., Yogeswaran, and Ponnambalam S.G. "Q-Learning Policies for Multi-Agent Foraging Task." In Communications in Computer and Information Science, 194–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15810-0_25.

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Xueke, Yang, Zhang Yu, Luo Junren, and Wang Kaiqiang. "Multi-agent Task Coordination Method Based on RCRS." In Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021), 2582–93. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9492-9_254.

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Тези доповідей конференцій з теми "Multi-Task agent"

Lavaur, Thomas, Déborah Conforto Nedelmann, Corentin Chauffaut, Jérôme Lacan, and Caroline P. C. Chanel. "Verifiable Multi-Agent Multi-Task Assignment." In 2024 IEEE Secure Development Conference (SecDev), 1–12. IEEE, 2024. http://dx.doi.org/10.1109/secdev61143.2024.00006.

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Takamizawa, Soya, Toru Namerikawa, and Shunsuke Tsuge. "Distributed Multi-Task Assignment for Multi-Agent Systems." In 2024 24th International Conference on Control, Automation and Systems (ICCAS), 1457–62. IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773086.

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Gao, Yu, Lizhong Zhu, Yunting Liu, and Jiaming Yang. "Multi-Agent Reinforcement Learning Based on Cross Task Information Sharing." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC), 970–74. IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691396.

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Zhang, Bin, Jinghao Long, and Duowen Chen. "Reliable Multi-agent Task Coordination Management System for Logistics System." In 2024 7th International Conference on Intelligent Robotics and Control Engineering (IRCE), 106–10. IEEE, 2024. http://dx.doi.org/10.1109/irce62232.2024.10739813.

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Wu, Chenhao, Jiang Liu, Kazutoshi Yoshii, and Shigeru Shimamoto. "Multi-objective Hierarchical Task Offloading in IoV: an Attentive Multi-agent DRL Approach." In 2024 IEEE 29th Asia Pacific Conference on Communications (APCC), 204–10. IEEE, 2024. https://doi.org/10.1109/apcc62576.2024.10767987.

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Yuan, Lei, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. "Multi-Agent Concentrative Coordination with Decentralized Task Representation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/85.

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Value-based multi-agent reinforcement learning (MARL) methods hold the promise of promoting coordination in cooperative settings. Popular MARL methods mainly focus on the scalability or the representational capacity of value functions. Such a learning paradigm can reduce agents' uncertainties and promote coordination. However, they fail to leverage the task structure decomposability, which generally exists in real-world multi-agent systems (MASs), leading to a significant amount of time exploring the optimal policy in complex scenarios. To address this limitation, we propose a novel framework Multi-Agent Concentrative Coordination (MACC) based on task decomposition, with which an agent can implicitly form local groups to reduce the learning space to facilitate coordination. In MACC, agents first learn representations for subtasks from their local information and then implement an attention mechanism to concentrate on the most relevant ones. Thus, agents can pay targeted attention to specific subtasks and improve coordination. Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods.

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Campbell, Adam, Annie S. Wu, and Randall Shumaker. "Multi-agent task allocation." In the 10th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389128.

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Xie, Rong, Daniela Rus, and Cliff Stein. "Scheduling multi-task multi-agent systems." In the fifth international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375735.376036.

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Rachmut, Ben, Sofia Amador Nelke, and Roie Zivan. "Asynchronous Communication Aware Multi-Agent Task Allocation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/30.

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Multi-agent task allocation in physical environments with spatial and temporal constraints, are hard problems that are relevant in many realistic applications. A task allocation algorithm based on Fisher market clearing (FMC_TA), that can be performed either centrally or distributively, has been shown to produce high quality allocations in comparison to both centralized and distributed state of the art incomplete optimization algorithms. However, the algorithm is synchronous and therefore depends on perfect communication between agents. We propose FMC_ATA, an asynchronous version of FMC_TA, which is robust to message latency and message loss. In contrast to the former version of the algorithm, FMC_ATA allows agents to identify dynamic events and initiate the generation of an updated allocation. Thus, it is more compatible for dynamic environments. We further investigate the conditions in which the distributed version of the algorithm is preferred over the centralized version. Our results indicate that the proposed asynchronous distributed algorithm produces consistent results even when the communication level is extremely poor.

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Yang, Guang, Vikram Kapila, and Ravi Vaidyanathan. "A Dynamic-Programming-Styled Algorithm for a Class of Multi-Agent Optimal Task Assignment." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/dsc-24536.

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Abstract In this paper, we use a dynamic programming formulation to address a class of multi-agent task assignment problems that arise in the study of fuel optimal control of multiple agents. The fuel optimal multi-agent control is highly relevant to multiple spacecraft formation reconfiguration, an area of intense current research activity. Based on the recurrence relation derived from the celebrated principle of optimality, we develop an algorithm with a distributed computational architecture for the global optimal task assignment. In addition, we propose a communication protocol to facilitate decentralized decision making among agents. Illustrative studies are included to demonstrate the efficacy of the proposed multi-agent optimal task assignment algorithm.

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Звіти організацій з теми "Multi-Task agent"

Kaplan, David J. The STAR System: A Unified Multi-Agent Simulation Model of Structure, Task, Agent, and Resource. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada519430.

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Kotenko, I. V. Formal Methods for Information Protection Technology. Task 2: Mathematical Foundations, Architecture and Principles of Implementation of Multi-Agent Learning Components for Attack Detection in Computer Networks. Part 2. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada427492.

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