A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page

Machine Learning for Next-Generation Intelligent Transportation Systems: A Survey

International audienceIntelligent Transportation Systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. It is expected that ITS will be an integral part of urban planning and future cities as it will contribute to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS poses a variety of challenges due to its scalability and diverse quality-of-service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of Machine Learning (ML), which has recently gained significant traction, to enable ITS. We provide a comprehensive survey of the current state-of-the-art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can use and benefit from ML technology

Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking

Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, model-based policies, as well as RD policies learned via single-agent DRL algorithms

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Studies on Complex and Connected Vehicle Traffic Networks

Transportation networks such as road networks are well-known for their complexity. Its users make choices of route, which mode to take, etc.; these users then interact with each other, producing emergent dynamics such as traffic jams on roads. These localized multi-user emergent physical phenomena then interact with similar group movements occurring in other locations, creating more complex network-scale dynamics. These patterns of hierarchical levels of organization and emergent phenomena at each level are typical of so-called "complex systems." In addition, the increasing adoption of information-technology systems like connected and autonomous vehicles is creating new challenges in modeling transportation networks, as new emergent behaviors become possible, but also provide new sources of information and possibilities for traffic operations management.The complexity of transportation networks precludes the use of a single all-encompassing theory for all situations at all scales. This dissertation describes several analyses into understanding and controlling emergent dynamics on road traffic networks. It is broken into three parts. The first part proposes models for several new phenomena at the "macroscopic," group-of-vehicles to group-of-vehicles, level. In particular, we solve a problem of modeling arbitrary road junctions with populations of behaviorally-heterogenous vehicles, where the vehicle flows are modelled by a continuum-approximation, partial-differential-equation-based model. We also present several new modeling constructions for a particular complex road network topology: freeways with managed lanes. It has been noted that these managed lane-freeway networks induce new emergent behaviors that are not present in traditional freeways; we propose modeling techniques for several of them, and fit them into traditional modeling paradigms.The second part presents several contributions for estimating the state of the macro-scale traffic dynamics on the road network, based on the micro-scale data of global navigational satellite system readings of the speed and position of individual vehicles. These contributions are extensions of the particle filtering mathematical framework. First, we demonstrate the use of a Rao-Blackwellized particle filter in assimilating vehicle-local speed measurements to better estimate the macroscopic density state of a freeway. Then, we propose new "hypothesis-testing" particle filters that can be used to reject outlier or otherwise malign measurements in a principled statistical manner.The third and final part presents two items on applying deep neural networks to transportation system problems at smaller scales. Both items make use of neural attention, which is a neural network design technique that allows for the integration of structural domain knowledge. First, we demonstrate the applicability of this technique towards estimating aggregate traffic states at the lane level, and present evidence that designing the neural network architecture to encode different types of lane-to-lane relationships (e.g., upstream lane vs neighboring lane) greatly benefits statistical learning. Then, we apply similar methods to an autonomous vehicle coordination problem in a deep reinforcement learning framework, and show that an attention-based neural network that allows each vehicle to attend to the other vehicles enables superior learning compared to a naive, non-attention-based architecture, and also allows principled generalization between varying numbers of vehicles

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence