For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one with a tight generalise/specialise loop.
But the scasp finds the two potential rules that cover both positive examples but not the negative example.
>> For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one with a tight generalise/specialise loop.
Yes! I'm thinking of how to adapt Louise (https://github.com/stassa/louise) to do that. The fact that s(CASP) is basically a Prolog-y version of ASP (with constraints) could make it a very natural sort of modification. Or, of course, there's always Well-Founded Semantics (https://www.swi-prolog.org/pldoc/man?section=WFS).
There was an earlier system called Thelma (https://github.com/stassa/thelma), an acronym for "Theory Learning Machine". Then I created a new system and, well, I couldn't resist a bad pun. They're a bit of a tradition in ILP.
See https://swish.swi-prolog.org/p/non-monotonic_ilp.swinb
For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one with a tight generalise/specialise loop.
But the scasp finds the two potential rules that cover both positive examples but not the negative example.
i.e.
flies(X,h8):-not penguin(X).
and
flies(X,h17):-bird(X),not penguin(X).
Which is cool.