Explanation-based Learning

\text { Hypothesis } \wedge \text { Descriptions } & \models \text { Classifications } \\ \text { Background } & \models \text { Hypothesis } \end{aligned}$$ **Intuition** Extracting general rules from individual observations. Converting first-principles theories into useful, special purpose knowledge. We can for example have a [[Knowledge Base (Prolog)]] with some [[First-Order Predicate Logic]] formulas. Then based on observations we can generate proof trees with the rules from the knowledge base. Such a tree can then be generalized by replacing any values with variables instead and creating a [[Konjunktion|Conjunction]] over all leaves. So the general process looks like this - Use background knowledge to construct a proof for the example. - In parallel, construct a generalized proof tree. - New rule is the conjunction of the leaves of the proof tree and the variabilized goal. - Drop conditions that are true regardless of the variables in the goal. #todo complexity stuff We use [[Memoization]].