Probabilistic Language

Probabilistic language focuses on representing and reasoning with uncertainty in symbolic systems, aiming to bridge the gap between logical reasoning and probabilistic inference. Current research emphasizes developing expressive probabilistic languages capable of handling complex causal relationships and integrating symbolic knowledge with neural network embeddings, often employing techniques like Markov Logic Networks and probabilistic programming. These advancements are crucial for improving the robustness and explainability of AI systems, particularly in applications like knowledge representation, decision-making under uncertainty, and natural language understanding. The ultimate goal is to create more human-like AI systems capable of nuanced reasoning and commonsense understanding.