Editing Symbolic artificial intelligence (section)

==== Reasoning in knowledge-based systems ====
{{Main|Reasoning system}}

Knowledge-based systems have an explicit [[knowledge base]], typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge. A separate [[inference engine]] processes rules and adds, deletes, or modifies a knowledge store.

[[Forward chaining]] inference engines are the most common, and are seen in [[CLIPS]] and [[OPS5]]. [[Backward chaining]] occurs in Prolog, where a more limited logical representation is used, [[Horn clause|Horn Clauses]]. Pattern-matching, specifically [[unification (computer science)|unification]], is used in Prolog.

A more flexible kind of problem-solving occurs when reasoning about what to do next occurs, rather than simply choosing one of the available actions. This kind of meta-level reasoning is used in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R may have additional capabilities, such as the ability to compile frequently used knowledge into higher-level [[Chunking (psychology)|chunks]].