Please join us for our next KRYPTON seminar of the Fall 2021 semester. We will meet at the usual time, 1600h, on Friday October 22, 2021. 

We will continue to have a virtual option, but will also meet in Room 2214 ENGR for those who want to attend in person. I will attend in person, but our speaker will be remote this week. 

You can check Krypton events through our calendar at:
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Krypton Seminar Series - Fall 2021

Date: October 22, 2021
Time: 4:00PM - 5:30PM

Venue: Zoom and Room 2241 ENGR

Link for remote participation:
Shou Matsumoto
Research Assistant Professor, C4I and Cyber Center, GMU
  and PhD graduate, Systems Engineering and Operations Research, GMU

Title — 
Dynamic Explanation of Bayesian Networks with Abductive Bayes Factor Qualitative Propagation and Entropy-Based Qualitative Explanation

Abstract — 
This presentation is a dry-run for the 24th International Conference on Information Fusion (FUSION 2021).

The success of Artificial Intelligence (AI) systems as decision aids is often largely contingent on the ability to trust their recommendations. This trust is greatly enhanced when the AI systems are able to provide explanations to justify the presented results which when implemented properly, also serve as a means to better understand unfamiliar domains. Unfortunately, underlying models in such systems can be often non-intuitive for humans and thus hard to interpret and explore. Explainable AI systems allow users to effectively understand, trust and operate the models. Under this context, a recognizable model for qualitatively displaying probabilistic information is the Bayesian Network (BN), which provides a graphical visualization of quantitative beliefs about conditional dependence and independence among random variables. We focus primarily on the dynamic explanation, which explains the reasoning process of a BN by exploring the means for analyzing the changes in the model in the light of new evidence. We extend Druzdzel’s concept about qualitative belief propagation, by introducing the idea of qualitative strength of edges in the active path, which is proportional to the Bayes factors of the Most Probable Explanation (MPE), or to the pairwise information entropy of variables (a.k.a. mutual information). We also present a full implementation in Java. 

Bio —Shou Matsumoto is a Research Assistant Professor at the C4I and Cyber Center at George Mason University. He earned his Ph.D. in Systems Engineering and Operations Research at George Mason University and an M.S. in Computer Science at Universidade de Brasília (2011, Brazil). Shou's fields of interest are  Artificial Intelligence and Software Product Line Engineering, and his recent researches involve topics like Multi-Entity Bayesian Networks, Multi-Entity Decision Graphs, probabilistic ontologies, explainable AI, domain engineering, SOA & BPM, object-oriented software design, model-based systems engineering, software development (Java), database management, and agile software development processes/management.  
Zoom Link
Meeting ID: 923 5693 8266
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