  Knowledge Representation and Reasoning and Machine Learning are two important
fields in AI. Nonmonotonic logic programming (NMLP) and Answer Set Programming
(ASP) provide formal languages for representing and reasoning with commonsense
knowledge and realize declarative problem solving in AI. On the other side,
Inductive Logic Programming (ILP) realizes Machine Learning in logic
programming, which provides a formal background to inductive learning and the
techniques have been applied to the fields of relational learning and data
mining. Generally speaking, NMLP and ASP realize nonmonotonic reasoning while
lack the ability of learning. By contrast, ILP realizes inductive learning
while most techniques have been developed under the classical monotonic logic.
With this background, some researchers attempt to combine techniques in the
context of nonmonotonic ILP. Such combination will introduce a learning
mechanism to programs and would exploit new applications on the NMLP side,
while on the ILP side it will extend the representation language and enable us
to use existing solvers. Cross-fertilization between learning and nonmonotonic
reasoning can also occur in such as the use of answer set solvers for ILP,
speed-up learning while running answer set solvers, learning action theories,
learning transition rules in dynamical systems, abductive learning, learning
biological networks with inhibition, and applications involving default and
negation. This workshop is the first attempt to provide an open forum for the
identification of problems and discussion of possible collaborations among
researchers with complementary expertise. The workshop was held on September
15th of 2013 in Corunna, Spain. This post-proceedings contains five technical
papers (out of six accepted papers) and the abstract of the invited talk by Luc
De Raedt.
