A Platform for Human-Machine Information Data Fusion

The overall goal in this concept paper is to present an innovative and rigorous mathematical methodology and an expert self learning data fusion and decision platform, which is scalable and effective for a variety of applications. This includes (i) Interface design that incorporates the understanding of how both machines and humans fuse soft and hard data and information, and (ii) Forming a shared perception and understanding of the environment between the human and the machine, which supports human decisions and reduces human soft and decision making errors. With this paper we continue our research on Uncertainty Balance Principle (UBP) which is at the core of our soft hard data fusion and decision making strategy. The proposed methodology can be employed in the context of Artificial Intelligence (AI) and Machine Learning (ML) applications, such as banking risk assessment, Block Chain peer to peer systems, ecological and climate modeling, social sciences, econometrics, as well as defense applications such as battle management.