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Auswahl der wissenschaftlichen Literatur zum Thema „Multi-Task agent“
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Zeitschriftenartikel zum Thema "Multi-Task agent"
Wu, Xiaohu, Yihao Liu, Xueyan Tang, Wentong Cai, Funing Bai, Gilbert Khonstantine und Guopeng Zhao. „Multi-Agent Pickup and Delivery with Task Deadlines“. Proceedings of the International Symposium on Combinatorial Search 12, Nr. 1 (21.07.2021): 206–8. http://dx.doi.org/10.1609/socs.v12i1.18585.
Der volle Inhalt der QuelleSurynek, Pavel. „Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering“. Proceedings of the International Symposium on Combinatorial Search 12, Nr. 1 (21.07.2021): 197–99. http://dx.doi.org/10.1609/socs.v12i1.18582.
Der volle Inhalt der QuelleXie, Bing, Xueqiang Gu, Jing Chen und 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, Nr. 6 (01.11.2018): 172988141881303. http://dx.doi.org/10.1177/1729881418813037.
Der volle Inhalt der QuellePei, Zhaoyi, Songhao Piao, Meixiang Quan, Muhammad Zuhair Qadir und 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, Nr. 2 (01.03.2020): 172988142092021. http://dx.doi.org/10.1177/1729881420920216.
Der volle Inhalt der QuelleThiele, Veikko. „Task-specific abilities in multi-task principal–agent relationships“. Labour Economics 17, Nr. 4 (August 2010): 690–98. http://dx.doi.org/10.1016/j.labeco.2009.12.003.
Der volle Inhalt der QuelleNedelmann, Déborah Conforto, Jérôme Lacan und 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 (30.05.2024): 396–404. http://dx.doi.org/10.1609/icaps.v34i1.31499.
Der volle Inhalt der QuelleRodiah, Iis, Medria Kusuma Dewi Hardhienata, Agus Buono und Karlisa Priandana. „Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, Nr. 6 (27.12.2022): 911–22. http://dx.doi.org/10.29207/resti.v6i6.4201.
Der volle Inhalt der QuelleWang, Yijuan, Weijun Pan und Kaiyuan Liu. „Multi-Agent Aviation Search Task Allocation Method“. IOP Conference Series: Materials Science and Engineering 646 (17.10.2019): 012058. http://dx.doi.org/10.1088/1757-899x/646/1/012058.
Der volle Inhalt der QuellePal, Anshika, Ritu Tiwari und Anupam Shukla. „Communication constraints multi-agent territory exploration task“. Applied Intelligence 38, Nr. 3 (15.09.2012): 357–83. http://dx.doi.org/10.1007/s10489-012-0376-6.
Der volle Inhalt der QuelleSurynek, Pavel. „Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 14 (18.05.2021): 12409–17. http://dx.doi.org/10.1609/aaai.v35i14.17472.
Der volle Inhalt der QuelleDissertationen zum Thema "Multi-Task agent"
Macarthur, Kathryn. „Multi-agent coordination for dynamic decentralised task allocation“. Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/209737/.
Der volle Inhalt der QuelleTurner, Joanna. „Distributed task allocation optimisation techniques in multi-agent systems“. Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36202.
Der volle Inhalt der QuelleKivelevitch, 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.
Der volle Inhalt der QuelleSuárez, 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.
Der volle Inhalt der QuelleDistributed 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.
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.
Der volle Inhalt der QuelleBasran, 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.
Der volle Inhalt der QuelleDay, 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.
Der volle Inhalt der QuelleAl-Karkhi, A. „Task recovery in self-organised multi-agent systems for distributed domains“. Thesis, University of Essex, 2018. http://repository.essex.ac.uk/22816/.
Der volle Inhalt der QuelleAhmadoun, 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.
Der volle Inhalt der QuelleTask 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
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.
Der volle Inhalt der QuelleIncludes 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.
Bücher zum Thema "Multi-Task agent"
Sinclair-Desgagne, Bernard. The first-order approach to multi-task principal-agent problems. Fontainebleau: INSEAD, 1991.
Den vollen Inhalt der Quelle findenKanakia, Anshul. Response Threshold Based Task Allocation in Multi-Agent Systems: Performing Concurrent Benefit Tasks with Limited Information. Nikolaus Correll dba Magellan Scientific, 2016.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Multi-Task agent"
Tkach, Itshak, und 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.
Der volle Inhalt der QuelleSingh, Arambam James, Poulami Dalapati und 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.
Der volle Inhalt der QuelleFaigl, Jan, Olivier Simonin und 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.
Der volle Inhalt der QuelleStrens, Malcolm, und 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.
Der volle Inhalt der QuelleChan, Chi-Kong, und 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.
Der volle Inhalt der QuelleSuzuki, Takahiro, und 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.
Der volle Inhalt der QuelleKarishma und 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.
Der volle Inhalt der QuelleZabłocki, 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.
Der volle Inhalt der QuelleM., Yogeswaran, und 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.
Der volle Inhalt der QuelleXueke, Yang, Zhang Yu, Luo Junren und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Multi-Task agent"
Lavaur, Thomas, Déborah Conforto Nedelmann, Corentin Chauffaut, Jérôme Lacan und 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.
Der volle Inhalt der QuelleTakamizawa, Soya, Toru Namerikawa und 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.
Der volle Inhalt der QuelleGao, Yu, Lizhong Zhu, Yunting Liu und 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.
Der volle Inhalt der QuelleZhang, Bin, Jinghao Long und 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.
Der volle Inhalt der QuelleWu, Chenhao, Jiang Liu, Kazutoshi Yoshii und 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.
Der volle Inhalt der QuelleYuan, Lei, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang und 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.
Der volle Inhalt der QuelleCampbell, Adam, Annie S. Wu und 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.
Der volle Inhalt der QuelleXie, Rong, Daniela Rus und 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.
Der volle Inhalt der QuelleRachmut, Ben, Sofia Amador Nelke und 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.
Der volle Inhalt der QuelleYang, Guang, Vikram Kapila und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "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, Februar 1999. http://dx.doi.org/10.21236/ada519430.
Der volle Inhalt der QuelleKotenko, 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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