Tutorials

Decision Modeling with DMN and OpenRules  (1h45m)
Jacob Feldman
This tutorial with introduce major business decision modeling concepts in the Decision Model and Notation (DMN) standard – see http://www.omg.org/spec/DMN/Current/. 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” (http://openrules.com). 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.
 
How to do it with LPS (Logic-Based Production System)  (3h)
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).
 
Semantic Event Reasoning and Rule-based Complex Event Processing  (3h)
Danh Le Phuoc, Adrian Paschke, Minh Dao-Tran, Marcin Wylot
This tutorial provides a comprehensive view on the recent research in the area of dynamic stream reasoning and semantic complex event processing. We cover a range from the basic concepts to the advanced techniques of the semantic stream data processing. We also go beyond the simple stream data processing aspects towards reasoning and complex event processing on streams of semantic data. The tutorial consists of two main parts: we will first introduce the basic architectures, data storage models and query processing strategies. The second part covers advanced topics like complex event processing and semantic reasoning over dynamic data. Both parts end with wrap-ups and open discussions on the emerging research problems in the field.
 
Logic-based Rule Learning for the Web of Data  (1h45m)
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.
 
Rulelog: Highly Expressive Semantic Rules with Scalable Deep Reasoning  (3h30m)
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.