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Watch a doctoral defense presentation exploring the integration of symbolic reasoning with neural networks through a process-grounded approach to neurosymbolic artificial intelligence. Learn about novel methodologies that combine the interpretability and logical reasoning capabilities of symbolic AI with the pattern recognition and learning strengths of neural networks, with particular emphasis on grounding these hybrid systems in real-world processes. Discover how this research addresses fundamental challenges in AI interpretability, reasoning under uncertainty, and the development of more robust and explainable AI systems. Examine the theoretical foundations, experimental methodologies, and practical applications of process-grounded neurosymbolic architectures, including their potential impact on domains requiring both data-driven learning and logical reasoning capabilities. Gain insights into the current state of neurosymbolic AI research, the specific contributions of this doctoral work, and future directions for advancing the field through process-aware integration of symbolic and connectionist approaches.
