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Nam Eung Hwang, Hyung Jun Kim and Jae Gwan Kim
In this paper, we propose a centralized task allocation and an alignment technique based on constraint table and alignment rules. For task allocation, a scoring scheme has to be set. The existing time-discounted scoring scheme has two problems; if the sc...
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Rachel Katoshevski-Cavari, Theo Arentze and Harry Timmermans
The relationship between various planning-ideas and sustainability is described, using a dedicated multi-agent model and demonstrated by a case study. The analysis supports planning based on preferences and behavior of a target population. Two objectives...
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Satoshi Warita and Katsuhide Fujita
Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commission...
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Naifeng Wen, Yundong Long, Rubo Zhang, Guanqun Liu, Wenjie Wan and Dian Jiao
This research introduces a two-stage deep reinforcement learning approach for the cooperative path planning of unmanned surface vehicles (USVs). The method is designed to address cooperative collision-avoidance path planning while adhering to the Interna...
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Okan Asik, Fatma Basak Aydemir and Hüseyin Levent Akin
The number of agents exponentially increases the complexity of a cooperative multi-agent planning problem. Decoupled planning is one of the viable approaches to reduce this complexity. By integrating decoupled planning with Monte Carlo Tree Search, we pr...
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Jun Long, Shimin Wu, Xiaodong Han, Yunbo Wang and Limin Liu
The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the...
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Cornelius Steinbrink, Marita Blank-Babazadeh, André El-Ama, Stefanie Holly, Bengt Lüers, Marvin Nebel-Wenner, Rebeca P. Ramírez Acosta, Thomas Raub, Jan Sören Schwarz, Sanja Stark, Astrid Nieße and Sebastian Lehnhoff
The complex nature of cyber-physical energy systems (CPES) makes systematic testing of new technologies for these setups challenging. Co-simulation has been identified as an efficient and flexible test approach that allows consideration of interdisciplin...
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Jian Zhang, Yaozong Pan, Ruili Wang, Yuqiang Fang and Haitao Yang
Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brute-fo...
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Ming Chong Lim and Han-Lim Choi
Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of...
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Mahdi Farnaghi and Ali Mansourian
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Rafael Cauê CARDOSO,Rafael Heitor BORDINI
Pág. 5 - 17
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Rachel Katoshevski, David Katoshevski, Theo Arentze, Harry Timmermans
Pág. 29 - 42
This paper analyzes 12 city plans that were developed based on environmental-sustainability indicators using a multi-agent model. The plans are based on three city forms and four types of city scenarios, each representing a different planning concept. Th...
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Konstantine Fines, Alexei Sharpanskykh and Matthieu Vert
Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning ...
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Qing Yang, Bingyu Song, Yingguo Chen, Lei He and Pei Wang
With the improvement of satellite autonomy, multi-satellite cooperative mission planning has become an important application. This requires multiple satellites to interact with each other via inter-satellite links to reach a consistent mission planning s...
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Qianqian Wu, Qiang Liu, Zefan Wu and Jiye Zhang
In the field of ocean data monitoring, collaborative control and path planning of unmanned aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In this study, we focus on how to utilize multiple UAVs to efficiently c...
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Yu Chen, Qi Dong, Xiaozhou Shang, Zhenyu Wu and Jinyu Wang
Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous su...
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Yogang Singh, Marco Bibuli, Enrica Zereik, Sanjay Sharma, Asiya Khan and Robert Sutton
Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface ve...
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David Rajaratnam, Torsten Schaub, Philipp Wanko, Kai Chen, Sirui Liu and Tran Cao Son
A warehouse delivery problem consists of a set of robots that undertake delivery jobs within a warehouse. Items are moved around the warehouse in response to events. A solution to a warehouse delivery problem is a collision-free schedule of robot movemen...
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Bernhard Jonathan Sattler, John Friesen, Andrea Tundis and Peter F. Pelz
Current challenges, such as climate change or military conflicts, show the great importance of urban supply infrastructures. In this context, an open question is how different scenarios and crises can be studied in silico to assess the interaction betwee...
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Shao Xuan Seah and Sutthiphong Srigrarom
This paper explores the use of deep reinforcement learning in solving the multi-agent aircraft traffic planning (individual paths) and collision avoidance problem for a multiple UAS, such as that for a cargo drone network. Specifically, the Deep Q-Networ...
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