There are 5 Tutorials at

June 20, 2017 12:38 PM
Register with RuleML+RR2017 ( to meet tutorial authors Robert Kowalski, Fariba Sadri, Miguel Calejo, Jacob Feldman, Francesca A. Lisi, Benjamin Grosof, Michael Kifer, Paul Fodor, Fabrizio Riguzzi, and Riccardo Zese.

Decision Modeling with DMN and OpenRules (1h45m)

by Jacob Feldman

This tutorial with introduce major business decision modeling concepts in the Decision Model and Notation (DMN) standard – see We will demonstrate the practical use of DMN by implementing various decision models using an popular open source Business Rules and Decision Management system “OpenRules” ( We will start with creation and testing of a simple decision model oriented to business people only. Then we will explain how the tested decision models can be integrate with IT systems. Then we will develop several more complex enough decision models demonstrating the power and applicability of different decision modeling constructs. We will end up with development of custom decisioning constructs that go beyond the DMN standard but support real-world decision modeling needs. All demonstrated decision models will be actually executed and analyzed with the audience during the presentation.

Logic-based Rule Learning for the Web of Data (1h45m)

by Francesca A. Lisi

The tutorial introduces to Inductive Logic Programming (ILP), being it a major logic-based approach to rule learning, and surveys extensions and applications of ILP to the Web of Data.

How to do it with LPS (Logic-Based Production System) (3h00m)

by Robert Kowalski, Fariba Sadri, Miguel Calejo

CLOUT is an open-source, web-based prototype of the computer language LPS (Logic-based Production System), implemented in SWISH. LPS includes both logic programming, which underpins the computer language Prolog, and a logical reconstruction of production systems, which are, arguably, the most popular computational model of human thinking. LPS fills the gap between imperative and logical languages, by viewing computation as generating state-transforming actions, to make goals, represented in logical form, true. This combination of logic and change of state makes LPS not only a programming, database, and AI knowledge representation and problem-solving language, but also a scaled-down model of human thinking.

The tutorial will present LPS by means of the web-based implementation, CLOUT, using such examples from programming, databases and AI, as sorting, dining philosophers, bank account maintenance, map colouring, the blocks world, and the prisoner’s dilemma. It will demonstrate the relationship between LPS and such other approaches to computing as production systems, reactive systems, abstract state machines and BDI agent languages. Moreover, it will show the close relationship between LPS, MetaTem, Transaction Logic and Abductive Logic Programming (ALP).

Rulelog: Highly Expressive Semantic Rules with Scalable Deep Reasoning (3h30m)

by Benjamin Grosof, Michael Kifer, Paul Fodor

In this half-day tutorial, we cover the fundamental concepts, key technologies, emerging applications, recent progress, and outstanding research issues in the area of Rulelog, a leading approach to fully semantic rule-based knowledge representation and reasoning (KRR). Rulelog matches well many of the requirements of cognitive computing. It combines deep logical/probabilistic reasoning tightly with natural language processing (NLP), and complements machine learning (ML). Rulelog interoperates and composes well with graph databases, relational databases, spreadsheets, XML, and expressively simpler rule/ontology systems – and can orchestrate overall hybrid KRR. Developed mainly since 2005, Rulelog is much more expressively powerful than the previous state-of-the-art practical KRR approaches, yet is computationally affordable. It is fully semantic and has capable efficient implementations that leverage methods from logic programming and databases, including dependency-aware smart cacheing and a dynamic compilation stack architecture.

Probabilistic Inductive Logic Programming on the Web (1h30m)

by Fabrizio Riguzzi, Riccardo Zese

Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this tutorial, we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER.