Case-based reasoning in depth
◈ 3 cardsThe retrieve→adapt→apply→save cycle and Kolodner’s retrieval heuristics; retrieval as weighted similarity search.
The CBR cycle
Case-based reasoning solves a new problem with the most similar past one. Its four steps form a loop:
- Retrieve the most similar stored case(s), using feature-based indexing.
- Adapt the retrieved solution to the differences in the new situation (analytic or heuristic transformation).
- Apply the adapted solution; if it fails, iterate.
- Save the new case — with its success/failure outcome — and update the index, so the system learns.
Step 4 is what distinguishes CBR from a static rule base: every problem solved (or botched) enlarges the experience the system draws on next time.
Retrieval is the hard part
Getting the right case back is the crux, because adaptation is only as good as the case you start from. Kolodner's six preference heuristics rank candidate cases:
- Goal-directed — same high-level goal as now.
- Salient-feature — match the most important features first.
- Specificity — prefer exact matches over general ones.
- Frequency — favour cases matched often.
- Recency — favour recently used cases.
- Ease-of-adaptation — favour cases easiest to adapt.
No single heuristic wins everywhere; real systems blend several into a weighted similarity score tuned to the domain — which is exactly the worked example below.
Worked example — weighted similarity retrieval
Give each feature a weight (its salience). A case's similarity to the query is the sum of the weights of the features it matches, divided by the total. With weights engine=0.5, lights=0.3, fuel=0.2 and a query engine=no_start, lights=dim, fuel=low, case#3 (engine=no_start, lights=dim, fuel=full) matches engine + lights = 0.5 + 0.3 = 0.8 and wins — even though no case matches every feature. That is the salient-feature heuristic in code: the two heavy features outvote the light one.