623.614 (18W) Current Topics in Artificial Intelligence
Überblick
- Lehrende/r
- LV-Titel englisch Current Topics in Artificial Intelligence
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 16 (24 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Englisch
- LV-Beginn 22.11.2018
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
At the end of the course, the students shall understand the benefits of various forms of product configuration and their embedding in Product Lifecycle Management and be able to select and use appropriate tools to find solutions for configuration requirements arising in practice.
Inhalt/e
Overview
Who needs configuration (i.e. individualization of products and services) and what for?
Typical examples from long-time experience with configuring complex technical systems.
Key technologies and useful tools for tackling product configuration problems.
Topics
What is product configuration? Real-word examples from mass customization to engineer-to-order.
Modelling of knowledge bases (product variability, constraints, logic), debugging and testing.
User interaction and use cases (check, solve, optimize, reconfigure).
Constraint Satisfaction Problem and variants (GCSP), Constraint Optimizing Problem.
Algorithms and performance: e.g. pre-compilation, local search, symmetry breaking.
Diagnosis of inconsistent configurations and knowledge bases.
Practical work with open-source tools having different strengths:
S.P.L.O.T - an online feature modelling tool
MiniZinc (alternatively Choco) - a classical constraint solver with a standard syntax
Potassco - an open-source toolset based on logic (ASP - answer set programming)
Erwartete Vorkenntnisse
Basic knowledge in knowledge and data engineering, logic programming, and Java.
Preparation by reading some information on tools before the course.
Presence during all 4 days (with your own notebook)!
Prüfungsinformationen
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Angewandte Informatik
(SKZ: 911, Version: 13W.1)
-
Fach: Business Information Systems
(Wahlfach)
-
Topics in Knowledge and Data Engineering for Business Information Systems (
2.0h VK / 4.0 ECTS)
- 623.614 Current Topics in Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
Topics in Knowledge and Data Engineering for Business Information Systems (
2.0h VK / 4.0 ECTS)
-
Fach: Business Information Systems
(Wahlfach)
- Masterstudium Angewandte Informatik
(SKZ: 911, Version: 13W.1)
-
Fach: Knowledge and Data Engineering
(Wahlfach)
-
Current Topics in Artificial Intelligence (
2.0h VK / 4.0 ECTS)
- 623.614 Current Topics in Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
Current Topics in Artificial Intelligence (
2.0h VK / 4.0 ECTS)
-
Fach: Knowledge and Data Engineering
(Wahlfach)
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Fach: Data and Knowledge Engineering
(Pflichtfach)
-
Artificial Intelligence (
2.0h VK / 4.0 ECTS)
- 623.614 Current Topics in Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
Artificial Intelligence (
2.0h VK / 4.0 ECTS)
-
Fach: Data and Knowledge Engineering
(Pflichtfach)
- Masterstudium Informationsmanagement
(SKZ: 922, Version: 13W.2)
-
Fach: Informationssysteme
(Pflichtfach)
-
2.8 Current Topics in Information Systems (
2.0h SE/VC/KS / 4.0 ECTS)
- 623.614 Current Topics in Artificial Intelligence (2.0h VC / 4.0 ECTS)
-
2.8 Current Topics in Information Systems (
2.0h SE/VC/KS / 4.0 ECTS)
-
Fach: Informationssysteme
(Pflichtfach)