6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework

Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird

Teaching Virtual Characters to use Body Language

Non-verbal communication, or “body language”, is a critical component in constructing believable virtual characters. Most often, body language is implemented by a set of ad-hoc rules.We propose a new method for authors to specify and refine their character’s body-language responses. Using our method, the author watches the character acting in a situation, and provides simple feedback on-line. The character then learns to use its body language to maximize the rewards, based on a reinforcement learning algorithm

Learning policy constraints through dialogue

Publisher PD

A model of the dynamics of organizational communication

We propose a model of the dynamics of organizational communication. Our model specifies the mechanics by which communication impact is fed back to communication inputs and closes the gap between sender and receiver of messages. We draw on language critique, a branch of language philosophy, and derive joint linguistic actions of interlocutors to explain the emergence and adaptation of communication on the group level. The model is framed by Te'eni's cognitive-affective model of organizational communication