7th Workshop on Dynamics of Knowledge and Belief (DKB-2018) and 6th Workshop KI & Kognition (KIK-2018):
Formal and Cognitive Reasoning
Workshop at the
September 24-28, 2018, Berlin, Germany
Information for real life AI applications is usually pervaded by uncertainty and subject to change, and thus demands for non-classical reasoning approaches. At the same time, psychological findings indicate that human reasoning cannot be completely described by classical logical systems. Sources of explanations are incomplete knowledge, incorrect beliefs, or inconsistencies. A wide range of reasoning mechanism has to be considered, such as analogical or defeasible reasoning. The field of knowledge representation and reasoning offers a rich palette of methods for uncertain reasoning both to describe human reasoning and to model AI approaches.
The aim of this series of workshops is to address recent challenges and to present novel approaches to uncertain reasoning and belief change in their broad senses, and in particular provide a forum for research work linking different paradigms of reasoning. We put a special focus on papers from both fields that provide a base for connecting formal-logical models of knowledge representation and cognitive models of reasoning and learning, addressing formal as well as experimental or heuristic issues. Previous events of the Workshop on "Dynamics of Knowledge and Belief" (DKB) took place in Osnabrück (2007), Paderborn (2009), Berlin (2011), and Koblenz (2013), previous editions of the Workshop on "KI & Kognition" (KIK) took place in Saarbrücken (2012), Koblenz (2013), and Stuttgart (2014), and joint workshops took place in Dresden (2015) and Dortmund (2017).We welcome original papers on the following and any related topics:
Abstract: In the classical symbolic AI approach to problem solving, knowledge is first acquired and represented, using some knowledge-representation language, and then applied to solve particular problems, using a knowledge-based or expert system. However, as the city planner Horst Rittel noticed in the early 1970s, in many problems domains, such as the law and city planning, the task of formulating or framing the problem, acquiring knowledge about the problem domain and applying this knowledge to solve the problem are deeply intertwined and interdependent. Rittel proposed argumentation as a more dynamic and iterative method suitable for solving such "wicked" problems. In this talk a formal, computational model of argument is presented which makes some progress on the road to providing a foundation for software tools which help people to collaboratively solve such problems. The model provides support for automatically constructing (inventing, generating) arguments, using argumentation schemes, as well as weighing and evaluating these arguments to determine which options proposed as solutions to a problem or issues have the best support.
Abstract: In the early days of machine learning, Donald Michie introduced two orthogonal dimensions to evaluate performance of machine learning approaches -- predictive accuracy and comprehensibility of the learned hypotheses. Later definitions narrowed the focus to measures of accuracy. As a consequence, statistical/neuronal approaches have been favored over symbolic approaches to machine learning, such as inductive logic programming (ILP). Recently, the importance of comprehensibility has been rediscovered under the slogan `explainable AI'. This is due to the growing interest in black-box deep learning approaches in many application domains where it is crucial that system decisions are transparent and comprehensible and in consequence trustworthy. I will give a short history of machine learning research followed by a presentation of two specific approaches of symbolic machine learning -- inductive logic programming and enduser programming. Furthermore, I will present current work on explanation generation.
The proceedings will be published in the CEUR Workshop Proceedings series (now available: CEUR Workshop Proceedings, Vol. 2194).
|Christoph Beierle||FernUniversität in Hagen, Germany|
|Gabriele Kern-Isberner||TU Dortmund, Germany|
|Marco Ragni||Universität Freiburg, Germany|
|Frieder Stolzenburg||Hochschule Harz, Germany|
|Matthias Thimm||Universität Koblenz-Landau, Germany|
|Thomas Barkowsky||Universität Bremen, Germany|
|Gerd Brewka||Universität Leipzig, Germany|
|Emmanuelle-Anna Dietz||TU Dresden, Germany|
|Christian Eichhorn||TU Dortmund, Germany|
|Christian Freksa||Universität Bremen, Germany|
|Ulrich Furbach||Universität Koblenz, Germany|
|Lupita Estefania Gazzo Castaneda||University of Giessen, Germany|
|Andreas Herzig||Universite Paul Sabatier, Toulouse, France|
|Steffen Hölldobler||Technische Universität Dresden, Germany|
|Haythem O. Ismail||German University in Cairo, Egypt|
|Manfred Kerber||University of Birmingham, UK|
|Ute Schmid||Universität Bamberg, Germany|
|Claudia Schon||Universität Koblenz-Landau, Germany|
|Holger Schultheis||Universität Bremen, Germany|
|Paul Thorn||Universität Düsseldorf, Germany|
|Hans Tompits||TU Wien, Austria|
|Christoph Wernhard||Technische Universität Dresden, Germany|
|Stefan Woltran||TU Wien, Austria|
|Deadline for Submission:||June 22, 2018|
|Notification of Authors:||August 08, 2018|
|Camera-ready Paper:||August 22, 2018|
|Workshop:||September 25, 2018|
Papers should be formatted according to the Springer LNCS format. The length of each paper should not exceed 8-12 pages. All papers must be written in English and submitted in PDF format via the EasyChair system.
Local information can be found on the web pages of the KI-2018 conference.
Last modified 2018-08-03