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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"

1

Wu, Xiaohu, Yihao Liu, Xueyan Tang, et al. "Multi-Agent Pickup and Delivery with Task Deadlines." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (2021): 206–8. http://dx.doi.org/10.1609/socs.v12i1.18585.

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Анотація:

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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2

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 (2021): 197–99. http://dx.doi.org/10.1609/socs.v12i1.18582.

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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.

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3

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 (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

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4

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 (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

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5

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

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6

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

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7

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 (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 overlappin

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8

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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9

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

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10

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 (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 sug

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Дисертації з теми "Multi-Task agent"

1

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 coordinatio

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2

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 re

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3

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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4

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 agent

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5

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 s

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6

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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7

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

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8

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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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-organis

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9

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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Анотація:

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, tr

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10

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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Анотація:

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 105-107).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>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

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Книги з теми "Multi-Task agent"

1

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

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2

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"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

Karishma and Shrisha Rao. "Cooperative Task Execution in Multi-agent Systems." In Lecture Notes in Computer Science. 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. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15147-2_7.

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9

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

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10

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). 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"

1

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). IEEE, 2024. http://dx.doi.org/10.1109/secdev61143.2024.00006.

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2

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). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773086.

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3

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). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691396.

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4

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). IEEE, 2024. http://dx.doi.org/10.1109/irce62232.2024.10739813.

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5

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). IEEE, 2024. https://doi.org/10.1109/apcc62576.2024.10767987.

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6

Yuan, Lei, Chenghe Wang, Jianhao Wang, et al. "Multi-Agent Concentrative Coordination with Decentralized Task Representation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/85.

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Анотація:

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

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7

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

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8

Xie, Rong, Daniela Rus, and Cliff Stein. "Scheduling multi-task multi-agent systems." In the fifth international conference. 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}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/30.

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Анотація:

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, wh

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10

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 facilita

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Звіти організацій з теми "Multi-Task agent"

1

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

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2

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. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada427492.

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