729 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning
Customer services are critical to all companies, as they may directly connect
to the brand reputation. Due to a great number of customers, e-commerce
companies often employ multiple communication channels to answer customers'
questions, for example, chatbot and hotline. On one hand, each channel has
limited capacity to respond to customers' requests, on the other hand,
customers have different preferences over these channels. The current
production systems are mainly built based on business rules, which merely
considers tradeoffs between resources and customers' satisfaction. To achieve
the optimal tradeoff between resources and customers' satisfaction, we propose
a new framework based on deep reinforcement learning, which directly takes both
resources and user model into account. In addition to the framework, we also
propose a new deep-reinforcement-learning based routing method-double dueling
deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate
our proposed framework and method using both synthetic and a real customer
service log data from a large financial technology company. We show that our
proposed deep-reinforcement-learning based framework is superior to the
existing production system. Moreover, we also show our proposed PER-DoDDQN is
better than all other deep Q-learning variants in practice, which provides a
more optimal routing plan. These observations suggest that our proposed method
can seek the trade-off where both channel resources and customers' satisfaction
are optimal.Comment: 13 pages, 7 figure
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