Computational Intelligent Systems: Evolving Dynamic Bayesian Networks

Dynamic Bayesian Networks (DBNs) are temporal probabilistic models for reasoning over time. They often formulate the core reasoning component of intelligent systems in the field of machine learning. Recent studies have focused on the development of some DBNs such as Hidden Markov Models (HMMs) and their variants, which are explicitly represented by highly skilled users and have gained popularity in speech recognition. These varieties of HMMs represented as DBNs have contributed to the baseline of temporal modelling. However they are limited in their expressive power as they are approximated and pose difficult challenges for users when choosing the appropriate model for diverse real-life applications. To worsen the situation further, researchers and practitioners have also stressed that applications often have difficulties when evolving (or learning) such network models from environments captured as massive datasets, due to the ongoing predominance of computational intensity (or nondeterministic polynomial (NP) time hard). Finding solutions to these challenges is a difficult task. In this thesis, a new class of temporal probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic Bayesian Networks (DBNs) are ideally suited to achieve situation awareness, in which elements in the environment must be perceived within a volume of time and space, their meaning understood, and their status predicted in the near future. The use of Dynamic Bayesian Networks in achieving situation awareness has been poorly explored in current research efforts. This research completely evolves DBNs automatically from any environment captured as multivariate time series (MTS) which minimizes the approximations and mitigates the challenges of choice of models. This potentially accommodates both highly skilled users and non-expert practitioners, and attracts diverse real-world application areas for DBNs. The architecture of our EDBN uses a combined strategy as it resolves two orthogonal issues to v address the challenging problems: (1) evolving DBNs in the absence of domain experts and (2) mitigating computational intensity (or NP-hard) problems with economic scalability. Most notably, the major contributions of this thesis are as follows: the development of a new class of temporal probabilistic modeling (EDBN), whose architecture facilitates the demonstration of its emergent situation awareness (ESA) and emergent future situation awareness (EFSA) technologies. The ESA and its variant reveal hidden patterns over current and future time steps respectively. Among other contributions are the development and integration of an economic scalable framework called dynamic memory management in adaptive learning (DMMAL) into the architecture of the EDBN to emerge such network models from environments captured as massive datasets; the design of configurable agent actuators; adaptive operators; representative partitioning algorithms which facilitate the scalability framework; formal development and optimization of genetic algorithm (GA) to emerge optimal Bayesian networks from datasets, with emphasis on backtracking avoidance; and diverse applications of EDBN technologies such as business intelligence, revealing trends of insulin dose to medical patients, water quality management, project profitability analysis, sensor networks, etc. To ensure the universality and reproducibility of our architecture, we methodically conducted experiments using varied real-life datasets and publicly available machine learning datasets mostly from the University of California Irvine (UCI) repository.