Novel Methods for Reasoning with Uncertain Hard and Soft Data using Probabilistic and Belief Theoretic Methods

Effectively combining multiple and complementary sources of information is becoming one of the most promising paths for increased accuracy and more detailed analysis in numerous applications. Neuroscience, business analytics, military intelligence, and sociology are among the areas that could significantly benefit from properly processing diverse data sources. However, traditional methods for combining multiple sources of information are based on slow or impractical methods that rely either on vast amounts of manual processing or on suboptimal representations of data. Moreover, most of the existing methods are not well suited for dealing with the increasing amount of human-generated data. We introduce an analytical framework that allows automatic and efficient processing of both hard (e.g., physics-based sensors) and soft (e.g., human-generated) information, leading to enhanced decision-making in multisource environments. This framework is based on the Dempster-Shafer (DS) Theory of Evidence as the common language for data representation and inference. To model and track uncertainties in soft data, our framework introduces Uncertain Logic, a classically consistent first order logic environment. In addition, our framework defines a filtering and tracking environment for incorporating both hard and soft data, where the probability posterior can be decomposed into a product of combining functions over subsets of the state and measurement variables. This combining function approach offers a framework for the development and incorporation of more sophisticated uncertainty modeling and tracking/estimation models, and at the same time allows incorporating and enhancing existing Bayesian methods. Future work is aimed at increasing the computational efficiency of the overall hard and soft data fusion framework.