Research map / Learning theory and algorithms / Combinatorial and search optimization
Representing and Learning Grammars in Answer Set Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| start from a narrower initial language (CFG a^n b^n c^m instead of a^i b^j c^k) (ablation) | ASG learner · a^n b^n c^n | total learning time: 5.1s -> 1.3s | Helped |
| include existing annotations (i=j) in the initial grammar (ablation) | ASG learner · a^n b^n c^n | total learning time: 5.1s -> 1.4s | Helped |
| widen the initial language to (a|b|c)* so the CFG is effectively learned (ablation) | ASG learner · a^n b^n c^n | total learning time: 5.1s -> 13314.9s | Hurt |
| restrict to Horn clauses with duplicate production rules given as background (ablation) | ASG learner · graph-col (3-colourability) | total learning time: 256.9s -> 23.8s | Helped |
| ASG learner vs LIG learner that learns whole grammar | ASG learner · copy language and a^n b^n c^n D | learning time: 1004s vs 18s on D | Mixed |
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