Lifelong Learning Networks: Beyond Single Agent Lifelong Learning

机译：Lifelong学习网络：超越单一代理终身学习

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Lifelong machine learning (LML) is a paradigm to design adaptive agents that can learn in dynamic environments. Current LML algorithms consider a single agent that has centralized access to all data. However, given privacy and security constraints, data might be distributed among multiple agents that can collaborate and learn from collective experience. Our goal is to extend LML from a single agent to a network of multiple agents that collectively learn a series of tasks.

机译：终身机器学习（LML）是设计可以在动态环境中学习的自适应代理的范例。当前的LML算法考虑一个具有对所有数据的集中访问的单个代理。但是，给定隐私和安全限制，数据可能分布在可以协作和学习的多个代理商之间的分布。我们的目标是将LML从单个代理扩展到多个代理网络，这些代理共同学习一系列任务。

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《AAAI Conference on Artificial Intelligence;Innovative Applications of Artificial Intelligence Conference;Symposium on Educational Advances in Artificial Intelligence》|2018年|7656-8223p|共2页
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Mohammad Rostami; Eric Eaton;
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中图分类 TP18-53;
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Lifelong Learning Networks: Beyond Single Agent Lifelong Learning

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