scholarly journals A Social Multi-Agent Cooperation System Based on Planning and Distributed Task Allocation

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 271
Author(s):  
Atef Gharbi

Planning and distributed task allocation are considered challenging problems. To address them, autonomous agents called planning agents situated in a multi-agent system should cooperate to achieve planning and complete distributed tasks. We propose a solution for distributed task allocation where agents dynamically allocate the tasks while they are building the plans. We model and verify some properties using computation tree logic (CTL) with the model checker its-ctl. Lastly, simulations are performed to verify the effectiveness of our proposed solution. The result proves that it is very efficient as it requires little message exchange and computational time. A benchmark production system is used as a running example to explain our contribution.




2013 ◽  
Vol 392 ◽  
pp. 366-373
Author(s):  
Tao Yang ◽  
Yong Jun Jia ◽  
Li Bo Yang

This paper proposes a decentralized control law of NlCyP&BG for a team of autonomous agents, which aims at achieving collective and uniform distribution around an appointed destination. A technique by virtual of coordinate constraints is described for eigenvalues derivation and contribution analysis, so that conditions for local asymptotical stability of n-agent system is deduced. Simulation work on a two-agent case and an extended four-agent case are displayed to prove the validity of stability conclusion, and at the same time the effectiveness of control law in accomplishing expected distribution and reorientation is verified exactly.



Author(s):  
Sebastian S. Rodriguez ◽  
Jacqueline Chen ◽  
Harsh Deep ◽  
Jaewook Lee ◽  
Derrik E. Asher ◽  
...  


Author(s):  
Philippe Laroque ◽  
Nathalie Gaussier ◽  
Nicolas Cuperlier ◽  
Mathias Quoy ◽  
Philippe Gaussier

AbstractStarting from neurobiological hypotheses on the existence of place cells (PC) in the brain, the aim of this article is to show how little assumptions at both individual and social levels can lead to the emergence of non-trivial global behaviors in a multi-agent system (MAS). In particular, we show that adding a simple, hebbian learning mechanism on a cognitive map allows autonomous, situated agents to adapt themselves in a dynamically changing environment, and that even using simple agent-following strategies (driven either by similarities in the agent movement, or by individual marks - “signatures” - in agents) can dramatically improve the global performance of the MAS, in terms of survival rate of the agents. Moreover, we show that analogies can be made between such a MAS and the emergence of certain social behaviors.



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