Publications (18)
Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors
Silvija Kokalj-Filipovic, Paul Toliver, William Johnson +1
Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring, and sensor data classi…
Foundations for a Probabilistic Event Calculus
Fabio Aurelio D'Asaro, Antonis Bikakis, Luke Dickens +1
We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information. It has an action language style syntax similar to t…
IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured Reasoning Templates
Aissatou Diallo, Antonis Bikakis, Luke Dickens +2
While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, understanding and validating their knowledge utilization remains challenging. Chain-of-thought (Co…
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
Silvija Kokalj-Filipovic, Rob Miller, Nicholas Chang +1
Adversarial examples in machine learning for images are widely publicized and explored. Illustrations of misclassifications caused by slightly perturbed inputs are abundant and com…
E-RES: A System for Reasoning about Actions, Events and Observations
Antonis Kakas, Rob Miller, Francesca Toni
E-RES is a system that implements the Language E, a logic for reasoning about narratives of action occurrences and observations. E's semantics is model-theoretic, but this implemen…
Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models
Aissatou Diallo, Antonis Bikakis, Luke Dickens +2
In this paper, we introduce Rule-Guided Feedback (RGF), a framework designed to enhance Large Language Model (LLM) performance through structured rule adherence and strategic infor…