Probabilistic Logic

Probabilistic logic studies how to combine symbolic rules and relations with probability, allowing systems to draw conclusions when facts are incomplete or uncertain. Approaches include Markov logic networks, probabilistic logic programming such as ProbLog, weighted model counting, lifted inference, and neural-symbolic methods that connect rules with learned components. These methods support tasks such as diagnosis, knowledge-graph reasoning, and causal or safety analysis. A central challenge is scaling reliable inference: exact reasoning can be expensive, while approximation and lifting bring tradeoffs, and probability alone may not determine a unique causal interpretation.