Iterative Learning of Answer Set Programs from Context Dependent Examples

Iterative Learning of Answer Set Programs from Context Dependent Examples

In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that som...

Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Over these years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastne...

Inductive Learning of Answer Set Programs

Communicating Answer Set Programs

Answer set programming is a form of declarative programming that has proven very successful in succinctly formulating and solving complex problems. Although mechanisms for representing and reasoning with the combined answer set programs of multiple agents have already been proposed, the actual gain in expressivity when adding communication has not been thoroughly studied. We show that allowing ...

Exploiting Vertical Parallelism from Answer Set Programs

In the last ten years we witnessed a rapid development of alternative logical systems, called non-monotonic logics (Baral & Gelfond 1994; Apt & Bol 1994; Minker 1993)--which allow new axioms to retract existing theorems, and result to be more adequate for common-sense reasoning and modeling dynamic knowledge bases. One of the outcomes of research in the field of non-monotonic logics is represen...