ILASP

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 (mark.law09@imperial.ac.uk). 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 this supplementary document.



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 this document.







Noisy Hamilton learning tasks

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.