Probabilistic Completeness

Probabilistic completeness explores the limitations of systems in achieving exhaustive or perfectly accurate outcomes, focusing on quantifying the probability of achieving a complete solution or the inevitability of incompleteness. Current research investigates this across diverse fields, including machine learning models (e.g., LLMs, graph neural networks), planning algorithms (e.g., Conflict-Based Search, sampling-based methods), and knowledge base management, often employing information-theoretic approaches or leveraging concepts from computational theory like Gödel's incompleteness theorems. Understanding and mitigating incompleteness is crucial for improving the reliability and trustworthiness of AI systems and for developing more robust methods in various scientific domains.