AbstractsComputer Science

Knowledge-empowered Probabilistic Graphical Models forPhysical-Cyber-Social Systems

by Pramod Anantharam

Institution: Wright State University
Year: 2016
Keywords: Computer Science; physical-cyber-social systems, probabilistic graphicalmodels, declarative knowledge, event extraction, eventinteractions, action recommendation, Internet of Things, SmartCity, learning with declarative knowledge, reasoning underuncertainty
Posted: 02/05/2017
Record ID: 2064283
Full text PDF: http://rave.ohiolink.edu/etdc/view?acc_num=wright1464417646


There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world constitute a Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) Automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT). Advisors/Committee Members: Sheth, Amit (Advisor), Thirunarayan, Krishnaprasad (Advisor), Barnaghi, Payam (Committee Chair).