Explanation-based learning & analogy (breadth)
◈ 3 cardsEBL (target / example / domain theory / operationality; generalize a proof) and structure-mapping analogy (Gentner). Recognition-level.
Knowledge-rich learning from one example
The methods so far were similarity-based — they need many examples and treat all features alike. Explanation-based learning (EBL) flips that: with strong domain knowledge, it can learn a general concept from a single example by explaining why that example is an instance, then generalizing the explanation.
EBL takes four inputs:
- a target concept — the thing to learn (e.g. “cup”);
- a training example — one instance (a specific red cup);
- a domain theory — rules that prove the example is an instance (“liftable + holds-liquid → cup”, etc.);
- an operationality criterion — the form the learned rule must take (use observable, structural features).
Worked example. EBL builds a proof tree showing the red cup satisfies “cup,” then generalizes the proof by replacing instance constants with variables. Crucially, the proof never used the cup’s color, so the generalized rule — “small, has-a-handle, concave-up bowl → cup” — correctly drops color. The domain theory tells EBL which features are causally relevant; similarity-based learning would need many examples to discover that color is irrelevant.
The catch (very examinable). EBL is speed-up learning, not new knowledge: every rule it produces was already in the deductive closure of the domain theory — the theory could have derived it without any example. The example just steers the prover to a useful, operational rule so you do not re-derive the proof each time. So EBL reformulates known knowledge for speed; it cannot discover facts the theory could not already entail.
Analogy — structure mapping (Gentner 1983)
Reasoning by analogy maps the relational structure of a known source onto an unfamiliar target. Structure-mapping theory says good analogies (1) drop surface attributes, (2) carry relations across, and (3) prefer higher-order relations — the systematicity principle. The atom-as-solar-system analogy is deep because it transfers a whole causal system (more-massive → attracts → revolves-around), not superficial likeness (“sunflowers look like the sun” transfers only a unary property).