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Explore logic's evolving role in AI, from truth calculus to event calculus, enabling advancements in probabilistic reasoning, machine learning, and explainable AI systems.
Explore causal objective functions, their optimization, and algorithmic complexities. Delve into structural causal models, unit selection, and variable elimination techniques for associational queries.
Explore a comprehensive theory of explainable AI using symbolic logic, covering necessary and sufficient conditions for decisions, minimal perturbations, and applications to various classifier types.
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