ILASP (Inductive Learning of Answer Set Programs) is a new
logic-based learning system capable of learning normal rules, choice
rules, constraints and weak constraints in ASP.
ILASP is available for download from here.
Bugs should also be reported on this page. ILASP is free to use for
research and education. If do use ILASP for research, we ask that you
use this citation of the
system, in addition to citing our relevant papers. Anyone wishing to
use ILASP for commercial purposes should contact Mark Law
(email@example.com). For details and examples of how to use
ILASP, please see the manual.
Details of Hypothesis Spaces from the paper "Inductive Learning of Answer Set Programs from Noisy Examples"
Details of the hypothesis spaces used in the above paper are given in
Tasks from the paper "Iterative Learning of Answer Set Programs from Context Dependent Examples"
We give here encodings of the tasks which were run for the paper
"Iterative Learning of Answer Set Programs from Context Dependent
Examples", which is to be presented at ICLP 2016. All tasks should be
run with ILASP v2.6.0. The encodings are explained in greater detail in
We give here encodings of the tasks which we use to evaluate the ILASP3
algorithm. All tasks are noisy versions of the Hamilton learning
setting, with 5% noise and varying numbers of examples.