Logic Programming

Logic programming studies how facts and rules can represent knowledge and support precise inference, with Prolog, Datalog, and answer set programming as major language families. Approaches range from improving solver efficiency and defining semantics for temporal, probabilistic, causal, and modular rules to learning rules with inductive or differentiable methods. Applications include legal reasoning, multiagent systems, databases, and neuro-symbolic tools that let language models delegate decisions to symbolic solvers. A central challenge is balancing expressive rules with tractable computation, while ensuring that learned or language-model-generated programs follow their intended semantics.