A multi-agent system “test bed” for evaluating autonomous agents

1997 ◽  
Author(s):  
J. M. Corchado ◽  
B. Lees ◽  
N. Rees
Keyword(s):  
Autonomous Agents ◽  
Multi Agent System ◽  
Test Bed ◽  
Agent System ◽  
Multi Agent

A Control Methodology for Autonomous Multi-Agent System in Collective and Uniform Distribution and Reorientation

2013 ◽  
Vol 392 ◽  
pp. 366-373
Author(s):  
Tao Yang ◽  
Yong Jun Jia ◽  
Li Bo Yang
Keyword(s):  
Uniform Distribution ◽  
Decentralized Control ◽  
Autonomous Agents ◽  
Asymptotical Stability ◽  
Control Law ◽  
Multi Agent System ◽  
Agent System ◽  
Contribution Analysis ◽  
Multi Agent ◽  
Control Methodology

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.

Measuring complacency in humans interacting with autonomous agents in a multi-agent system

2020 ◽  
Author(s):  
Sebastian S. Rodriguez ◽  
Jacqueline Chen ◽  
Harsh Deep ◽  
Jaewook Lee ◽  
Derrik E. Asher ◽  
...  
Keyword(s):  
Autonomous Agents ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

AN ARCHITECTURE FOR A MULTI-AGENT SYSTEM TEST-BED

2002 ◽  
Vol 35 (1) ◽  
pp. 441-446 ◽  
Author(s):  
L. Motus ◽  
M. Meriste ◽  
T. Kelder ◽  
J. Helekivi
Keyword(s):  
Multi Agent System ◽  
Test Bed ◽  
Agent System ◽  
Multi Agent

EFFICIENCY ANALYSIS OF THE TIME-SHUFFLING METHOD FOR THE EVOLUTION OF AGENT BEHAVIOR

2012 ◽  
Vol 23 (02) ◽  
pp. 523-542
Author(s):  
PATRICK EDIGER ◽  
ROLF HOFFMANN
Keyword(s):  
Autonomous Agents ◽  
State Machine ◽  
State Machines ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Behavior ◽  
Local View ◽  
Finite State ◽  
Multi Agent ◽  
Algorithm Scheme

We have analyzed the effectiveness and the efficiency of a time-shuffling method applied to an evolutionary algorithm scheme in order to optimize the behavior of autonomous agents in a multi-agent system. The multi-agent system is modeled as cellular automata (CA) because of the inherent parallelism of the model, which suits well the requirements of a system of autonomous moving agents with a local view. The task of the agents is the all-to-all communication, i.e., all agents shall communicate their initially mutually exclusive information to all other agents. The agents' uniform behavior is defined by a finite-state machine, which is evolved by a genetic algorithm (GA). 20 different initial two-dimensional environments were defined as a training set, 10 of them with border, 10 with cyclic wrap-around. The state machine was evolved (1) directly by a GA for all 20 environments, and (2) indirectly by two separate GAs for the 10 environments with border and the 10 environments with wrap-around, with a subsequent time-shuffling technique in order to integrate the good abilities from both of the separately evolved state machines. The time-shuffling technique alternates two state machines periodically. The results show that time-shuffling two separately evolved state machines is effective and much more efficient than the direct application of the GA.

scholarly journals Reactive multi-agent system applied to self-healing in Smart Grids

2020 ◽  
Vol 27 (2) ◽  
pp. 127-139
Author(s):  
Italo Ramon da Costa Campos ◽  
Filipe Saraiva
Keyword(s):  
Smart Grids ◽  
Autonomous Agents ◽  
Healing Process ◽  
Computational Simulation ◽  
Network Models ◽  
Work Load ◽  
Self Healing ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

This paper presents a decentralized algorithm for application in the smart grids self-healing problem, at the distribution level. The algorithm implementation is made using a reactive multi-agent system, which models the electrical grid in terms of autonomous agents which perform the algorithm operations in a distributed and parallel way. To validate this algorithm, two distribution network test models are used: a 15 bus model and a 33 bus model — standardized by IEEE. The results are obtained by means of computational simulation and shown in this paper, to each one of the network models. The results show that the proposed approach is able to recover all the nodes of the grid, within the simulation conditions. Moreover, it is seen that the multi-agent system directs the work load exactly to the failure point, preventing the involvement of the entire grid to the self-healing process.

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