Shweta Singh PhD Dissertation Defense

Announcing:
PhD Dissertation Defense

Name:
Shweta Singh

Title:
A Qualitative Approach for Learning and Detection of Emergent Behaviors

Date:
4/25/2024

Time:
9:30:00 AM

Committee Members:
Prof. Mieczyslaw M. Kokar (Advisor)
Prof. Taskin Padir
Dr. Paul Kogut

Abstract:
Emergence has been studied in various fields, including engineering, computer science, and economics. However, there is no agreed-upon definition of what emergence means. As a result, it remains a debatable topic among researchers who want to understand and use emergence to engineer their systems. In this thesis, we are targeting two issues related to the domain of emergence.
First, we introduce a framework that enables researchers to encode key aspects of emergence theories into an ontology using the Emergence Metaontology. This metaontology provides the basic vocabulary specialized to the domain of emergence. Researchers will be able to add their theories to those that are already encoded, and then use queries to examine and compare these theories. OWL reasoners can infer new facts and possibly identify inconsistencies between conflicting theories. This will allow researchers to gain a greater understanding of the existing emergence theories. To the best of our knowledge, this research is the first attempt in the emergence domain to use a query-supported ontological approach to encode and compare multiple theories of emergence.
The second contribution is algorithms for detecting the onset of emergence at the beginning of a possibly irreversible emergent behavior. The approach to accelerate the detection of emergence is based on transforming the values of the variables of a system into a different space and then running detection in that space. The first transformation relies on the property of self-similarity – when the values of system variables are in a specific relation. Such relations formalize hypersurfaces in the quantitative system space. The second kind of transformation utilizes qualitative abstraction of the general dynamical system using the $Q^2$ (Quantitative-Qualitative) approach. The quantitative variables of a general dynamical system are mapped to qualitative variables (hypersurfaces), leading to the representation of the monitored dynamical system as a Qualitative Dynamical system (QDS).  Detection of emergence is then implemented as a process of analyzing the behavior of the QDS. The efficiency of the approach has been validated on multiple simulations of dynamical systems.