DISSERTATION DEFENSE

Author : Chathurangi Jayakody Kankanamalage
Advisors: Dr. Amit Sheth
Date: October 13, 2026
Time: 09:30 am
Location: 529 Seminar Room, AI Institute
Link:   https://sc-edu.zoom.us/j/84342587325

Abstract
Modern manufacturing systems are complex cyber–physical environments in which rare failures, changing operating conditions, and interdependent processes make reliable decision-making challenging. Although neural models achieve strong predictive performance, they often function as isolated components with limited support for causal reasoning, interpretable explanations, and process constraints. This dissertation addresses these limitations through the design and operationalization of neurosymbolic multi-agent copilots for industrial systems.

The central thesis is that integrating data-driven learning with symbolic process knowledge, causal reasoning, and coordinated agents can improve the robustness, intelligence, and operational trustworthiness of industrial decision support. The dissertation develops this thesis through five interconnected contributions.

First, it characterizes rare events in industrial data and develops structured data enrichment methods to support learning under severe class imbalance. Second, it develops specialized predictive models for anomaly detection, anomaly prediction, and forecasting, demonstrating the effectiveness of compact, task-aware architectures relative to larger foundation models on the evaluated industrial tasks. Third, it introduces knowledge-guided inference methods that combine neural predictions with process ontologies, knowledge graphs, and causal models to support logically consistent predictions, root-cause analysis, and counterfactual reasoning. Fourth, it develops multi-agent copilot architectures that coordinate specialized agents through shared knowledge and adaptive workflows. Fifth, it advances operationalization through evidence-grounded industrial question answering, agent benchmarking, and staged deployment frameworks.

Evaluations using real industrial datasets and laboratory manufacturing environments demonstrate improvements over task-specific baselines in predictive performance, reasoning consistency, and agent coordination. They also reveal remaining limitations in root-cause identification and agent evaluation. Across these contributions, process constraints, traceable evidence, and human oversight are integrated into system design to support operational trustworthiness. The dissertation provides methods and practical frameworks for advancing neurosymbolic multi-agent systems toward reliable use in industrial decision-making.
 

DISSERTATION DEFENSE

Author :Nawras Alkassab
Advisors: Dr. Chin-Tser Huang
Date: October 19, 2026
Time: 03:15 pm
Location: Room 2267, Storey Innovation building

Abstract
In psychology, humans who are intrinsically motivated tend to outperform those whose self-validation is more dependent on extrinsic rewards. In reinforcement learning, curiosity-driven agents suffer from the noisy TV problem, since calculating the error in predicting the future observations leaves the agents perplexed in extremely randomized environments. In this work, we tackle the video prefetching problem at edge networks, an NP-Hard problem, using intrinsically motivated reinforcement learning agents. The extrinsic rewards in the video prefetching problem are sparse and delayed, since they are received from access networks by edge networks infrequently. First, we explore the benefits and schemes of video prefetching from cloud networks to edge networks. Next, we explore the design and implementation of deep reinforcement learning for video prefetching at edge networks. By formulating the prefetching problem at edge networks as Partially Observable Markov Decision Process, we propose an intrinsically motivated reinforcement learning agent, Techie, to maximize both prefetching accuracy and prefetching coverage. Techie self-tunes its aggressiveness to manage the trade-offs between prefetching accuracy and prefetching coverage, given a long-term trajectory of video requests. Finally, we conclude that the prefetching problem at edge networks is susceptible to large action space and observation space, which exponentially increases as the size of the edge network's storage increases linearly. In addition, we believe that intrinsically motivated reinforcement learning agents for video prefetching at edge networks offer multiple benefits such as their sensitivity to the popularity of video items issued by end-users at different heterogeneous access networks and their ability to make intelligent prefetching decisions online without relying on access networks' metrics such as Quality of Experience metrics and bandwidth wastage.