Reasoning under partial observability in heterogeneous networked systems
Anwar, Mubashir
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Permalink
https://hdl.handle.net/2142/132709
Description
Title
Reasoning under partial observability in heterogeneous networked systems
Author(s)
Anwar, Mubashir
Issue Date
2025-12-11
Director of Research (if dissertation) or Advisor (if thesis)
Caesar, Matthew
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Heterogeneous networked systems
Reasoning under uncertainty
Declarative verification
Abstract
Networked systems are collections of interconnected devices and software components that communicate to provide coordinated functionality, such as sensing, control, data processing, and access enforcement. Many everyday services rely on such systems, including building automation, cloud applications, industrial monitoring, and connected medical or sensor devices. In these systems, overall behavior is shaped by configuration choices, such as access rules, control logic, and communication settings, and by how these choices interact across components. Small configuration mistakes or unintended rule interactions can therefore lead to serious safety or security problems. These systems are often assembled from components developed independently and are not always fully observable during operation, which makes it difficult to determine whether the system as a whole behaves correctly and safely.
This thesis addresses the problem of reasoning about correctness, safety, and redundancy in configured networked systems under uncertainty. It argues that this problem can be addressed by introducing a common, easy-to-use semantic layer into which heterogeneous configurations can be compiled, and which explicitly supports the representation of incomplete information. Such a semantic layer enables sound, system-wide reasoning over the set of possible behaviors consistent with what is known about the system.
To realize this approach, the thesis leverages conditional tables, a database-inspired representation for incomplete information. Conditional tables provide a unifying symbolic abstraction for modeling heterogeneous data and control logic while explicitly encoding uncertainty. Within this framework, properties such as safety, policy compliance, and reachability can be expressed as declarative queries and evaluated soundly under partial observability. The same representation supports semantic minimization, allowing complex systems to be reduced to smaller models that preserve behaviors of interest. A prototype implementation demonstrates that conditional-table–based reasoning is practical and scales to realistic networked scenarios under typical deployment sizes.
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