Skip to main content
Alle Ausgaben dieser Zeitschrift
Zum Archiv

KI - Künstliche Intelligenz OnlineFirst articles

10.02.2017 | Community


01.02.2017 | Technical Contribution Open Access

Identifying Landmark Candidates Beyond Toy Examples

A Critical Discussion and Some Way Forward

Incorporating references to landmarks in navigation systems requires having data on potential landmarks in the first place. While there have been many approaches in the scientific literature for identifying landmark candidates, these have hardly …

23.01.2017 | Technical Contribution

Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair

This paper investigates different approaches for handling inconsistent DL-Lite knowledge bases in the case where the assertional base is prioritized and inconsistent with the terminological base. The inconsistency problem often happens when the …

19.01.2017 | Interview Open Access

Indoor Wayfinding: Interview with Christoph Hölscher and Ruth Conroy Dalton

02.01.2017 | Survey

Many Facets of Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences: A Brief Survey

In this paper, we give an introduction to reasoning under uncertainty, inconsistency, vagueness, and preferences in artificial intelligence (AI), including some historic notes and a brief survey to previous approaches.

Aktuelle Ausgaben

Über diese Zeitschrift

The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.


Higher-Level Cognition and Computation

Human higher-level cognition is a multi-faceted and complex area of thinking which includes the mental processes of reasoning, decision making, creativity, and learning among others. Logic, understood as a normative theory of thinking, has a widespread and pervasive effect on the foundations of cognitive science. However, human reasoning cannot be completely described by logical systems. Sources of explanations are incomplete knowledge, incorrect beliefs, or inconsistencies. Still, humans have an impressive ability to derive satisficing, acceptable conclusions. Generally, people employ both inductive and deductive reasoning to arrive at beliefs; but the same argument that is inductively strong or powerful may be deductively invalid. Therefore, a wide range of reasoning mechanism has to be considered, such as analogical or defeasible reasoning.

The topics of interest include, but are not limited to:

• Analogical reasoning
Common sense and defeasible reasoning
Deductive calculi for higher-level cognition
• Inductive reasoning and cognition
• Preferred mental models and their formalization
• Probabilistic approaches of reasoning

The Künstliche Intelligenz journal, which is published and indexed by Springer, supports the following lists of formats: technical contributions, research projects, discussions, dissertation abstracts, conference reports and book reviews. If you are interested in contributing to this special issue, please contact one of the guest editors:

Dr. Marco Ragni
University of Freiburg
Center for Cognitive Science

Institute of Computer Science and Social Research
Friedrichstr. 50
D-79098 Freiburg, Germany
Prof. Frieder Stolzenburg
Harz University of Applied Sciences
Automation & Computer Sciences Dep.
Friedrichstr. 57-59
38855 Wernigerode, Germany

Important dates:

• Statement of interest: 15-Aug-2014
• Submission deadline: 07-Oct-2014
• Notification: 15-Nov-2014
• Camera-ready copy: 15-Jan-2015
• Special issue: KI 3/2015

Submission and contribution format:

The articles should be written in english, in order to attract an international audience, formatted with the Springer LaTeX package for journals (\[).

Submissions should be sent as pdf file to

Advances in Autonomous Learning

Autonomous Learning research aims at understanding how adaptive systems can efficiently learn from the interaction with the environment, especially by having an integrated approach to decision making and learning, allowing systems to decide by themselves on actions, representations, hyper-parameters and model structures for the purpose of efficient learning.

It addresses challenges such as how to autonomously learn representations for efficient model use, how to arrive at suitable cost functions from meta-objectives (generalizing inverse RL), how to autonomously choose model structures and hyper-parameters in possibly non-stationary environments, or how to design efficient actor-reward strategies which generalize across tasks.

Application scenarios which require these type of complex models span high-impact domains such as robotics, life-long learning, intelligent tutoring, or big data analytics. We invite contributions related to the following non exhaustive list of topics:

– autonomous learning of rich data representations,
– active learning in structured (e.g., hybrid, relational) interactive domains,
– learning models with autonomous complexity adaptation,
– transfer learning,
– structure learning,
– statistical relational learning,
– theoretical advances to measure model autonomy,
– applications and project reports in the field of autonomous learning.

Prof. Barbara Hammer
Universität Bielefeld
D-33594 Bielefeld
Prof. Marc Toussaint

Universität Stuttgart
D-70569 Stuttgart

Companion Technologies

At present, we observe a rapid growth in the development of increasingly complex ‘‘intelligent’’ systems that serve users throughout all areas of their daily life. They range from classical technical systems such as household devices, cars, or consumer electronics through mobile apps and services to advanced service robots in various fields of application. While many of the rather conventional systems already provide multiple modalities to interact with, the most advanced are even equipped with cognitive abilities such as perception, cognition, and reasoning. However, the use of such complex technical systems and in particular the actual exploitation of their rich functionality remain challenging and quite often lead to users’ cognitive overload and frustration.

Companion Technologies aim at bridging the gap between the extensive functionality of technical systems and human users’ individual requirements and needs. They enable the construction of really smart—adaptive, flexible, and cooperative—technical systems by employing a combination of AI techniques and relying on psychological and neurobiological findings.

The special issue ‘‘Companion Technologies’’ of the KI Journal aims to present ongoing research, application perspectives, and other insights into an exciting research area emerging across the fields of Artificial Intelligence, Cognitive Psychology, and Cognitive Sciences. Topics of interest include, but are not limited to:
• Computational models of cognitive processes
• Reasoning for adaptive systems
• User-centered planning
• Multi-modal emotion and motivation recognition
• Knowledge-based human–computer interaction
• Knowledge-based dialogue management
• Cooperative and adaptive systems

The KI Journal, published and indexed by Springer, supports a variety of formats including technical articles, project descriptions, surveys, dissertation abstracts, conference reports, and book reviews.
Interested authors are asked to contact the guest editors at their earliest convenience:

Prof. Dr. Susanne Biundo-Stephan
Institute of Artificial Intelligence
Ulm University
89069 Ulm

Daniel Höller
Institute of Artificial Intelligence
Ulm University
89069 Ulm

Pascal Bercher
Institute of Artificial Intelligence
Ulm University
89069 Ulm

Weitere Informationen

Premium Partner

GI Logo

BranchenIndex Online

Die B2B-Firmensuche für Industrie und Wirtschaft: Kostenfrei in Firmenprofilen nach Lieferanten, Herstellern, Dienstleistern und Händlern recherchieren.

Zur B2B-Firmensuche