September 15, 2013, Corunna, Spain, in association with LPNMR 2013

Aims and Scope

Knowledge representation and reasoning (KR&R) and machine learning are two important fields in artificial intelligence (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 inductive 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 (inductive) learning. By contrast, ILP realizes inductive machine 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 inductive logic programming (NMILP). 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 to use existing solvers. Cross-fertilization between learning and nonmonotonic reasoning can also occur in such as:

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. To facilitate interactions between researchers in the areas of (machine) learning and nonmonotonic reasoning, we welcome contributions focusing on problems and perspectives concerning both learning and nonmonotonic reasoning.

Invited Talk (new)

Luc De Raedt (Katholieke Universiteit Leuven, Belgium)
"Declarative Modeling for Machine Learning and Data Mining"

Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. Throughout the talk, I shall use illustrations from our work on constraint programming for itemset mining and probabilistic programming.

Programme (new)

9:20 Opening
9:30 (Invited Talk) Luc De Raedt:
Declarative Modeling for Machine Learning and Data Mining
Session 1: Leaning and ASP (10:20-11:35)
10:20 Luc De Raedt, Sergey Paramonov, Matthijs van Leeuwen:
Relational Decomposition Using Answer Set Programming
10:45 Alessandra Mileo, Matthias Nickles:
Probabilistic Inductive Answer Set Programming by Model Sampling and Counting
11:10 Alexandre Rocca, Tony Ribeiro, Katsumi Inoue:
Inference and Learning of Boolean Networks Using Answer Set Programming
11:35 Coffee Break
Session 2: Biology and Representation (12:00-13:15)
12:00 Robert Demolombe, Luis Farinas del Cerro, Naji Obeid:
Molecular Interaction Automated Maps
12:25 Adrien Rougny, Christine Froidevaux, Yoshitaka Yamamoto, Katsumi Inoue:
Translating the SBGN-AF Language into Logic to Analyze Signalling Networks
12:50 Yoshitaka Yamamoto, Koji Iwanuma, Hidetomo Nabeshima:
Practically Fast Non-Monotone Dualization Based on Monotone Dualization
13:15 Closing


We solicit original papers which are not published elsewhere. Papers should be written in English and be formatted according to the Springer Verlag LNCS style, which can be obtained from Every paper should not exceed 12 pages including the title page, references and figures. All submissions will be peer-reviewed and all accepted papers must be presented at the workshop.

Submission page:

Proceedings (new)

The online proceedings are available here for the workshop, and will be published in CoRR (Computing Research Repository - arXiv) afterwards.

Important Dates

Paper registration: June 30 Extended to July 21
Submission deadline: July 7 Extended to July 28
Notification: August 24
Final version due: September 7
Workshop: September 15

Workshop co-Chairs

Katsumi Inoue, National Institute of Informatics, Japan
Chiaki Sakama, Wakayama University, Japan

Program Committee