Consensus-Based Learning for MAS: Definition, Implementation and Integration in IVEs

被引:5
作者
Carrascosa, C. [1 ]
Enguix, F. [1 ]
Rebollo, M. [1 ]
Rincon, J. [1 ]
机构
[1] Univ Politecn Valencia, VRAIn Valencian Res Inst Artificial Intelligence, Valencia, Spain
来源
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE | 2023年 / 8卷 / 03期
关键词
FRAMEWORK;
D O I
10.9781/ijimai.2023.08.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
One of the main advancements in distributed learning may be the idea behind Google's Federated Learning (FL) algorithm. It trains copies of artificial neural networks (ANN) in a distributed way and recombines the weights and biases obtained in a central server. Each unit maintains the privacy of the information since the training datasets are not shared. This idea perfectly fits a Multi-Agent System, where the units learning and sharing the model are agents. FL is a centralized approach, where a server is in charge of receiving, averaging and distributing back the models to the different units making the learning process. In this work, we propose a truly distributed learning process where all the agents have the same role in the system. We suggest using a consensus-based learning algorithm that we call Co-Learning. This process uses a consensus process to share the ANN models each agent learns using its private data and calculates the aggregated model. Co-Learning, as a consensus-based algorithm, calculates the average of the ANN models shared by the agents with their local neighbors. This iterative process converges to the averaged ANN model as a central server does. Apart from the definition of the Co-Learning algorithm, the paper presents its integration in SPADE agents, along with a framework called FIVE allowing to develop Intelligent Virtual Environments for SPADE agents. This framework has been used to test the execution of SPADE agents using Co-Learning algorithm in a simulation of an orange orchard field.
引用
收藏
页码:21 / 32
页数:12
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