A Novel Distributed Multi Agent System Plan Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 1732-1735
Author(s):  
Wei Jin Jiang ◽  
Xin Mi Luo ◽  
Li Na Yao

This paper presents One-Distributed-Multi-Agent-Plan. In this system, each agent is planning to carry out their goals and bound by the plan and to determine action steps. Action steps with the routine sequence of steps bound to the time sequence Equivalence of constraints and variables used restraint is not equivalent to not entirely routine steps; Programming is gradually adding and refining constraints on the planning process, algorithm based on the classic UCPOP Planning algorithm; through a special Multi-Agent arrange to check and clear up collision between Agent-Plan, then restriction their action. In this algorithm, The coordination and Conflict-Detection of the Agent-Plan consulted by Multi-Agent I.e.; the bound consistency of Distributed judgment between Agent-Plan; The algorithm is reliable in determining the environment, Since the algorithm is exchanged between agents and actions related to the conflict, and the causal chain bound Therefore it is a small amount of communication and high security advantages.

2013 ◽  
Vol 40 (10) ◽  
pp. 3858-3871 ◽  
Author(s):  
Harry K.H. Chow ◽  
Winson Siu ◽  
Chi-Kong Chan ◽  
Henry C.B. Chan

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7896
Author(s):  
Jiyoun Moon

As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment.


2009 ◽  
Vol 2 (4) ◽  
pp. 61-70
Author(s):  
Ravi Babu Pallikonda ◽  
◽  
K. Prapoorna ◽  
N.V. Prashanth ◽  
A. Shruti ◽  
...  

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