7th Workshop on Dynamics of Knowledge and Belief (DKB-2018)   and   6th Workshop KI & Kognition (KIK-2018): Formal and Cognitive Reasoning Workshop at the 41st German Conference on Artificial Intelligence (KI-2018) September 24-28, 2018, Berlin, Germany Organized by the FG Wissensrepräsentation und Schließen and FG Kognition of the GI
[ Call for Papers ] [ Committee ] [ Dates ] [ Submission ] [ Program ] [ Local Information ]

Aims and Scope

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.

Call for Papers

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).

• Action and change
• Agents and multiagent systems
• Analogical reasoning
• Argumentation theories
• Belief revision and belief update
• Cognitive modeling and empirical data
• Common sense and defeasible reasoning
• Decision theory and preferences
• Inductive reasoning and cognition
• Knowledge representation in theory and practice
• Learning and knowledge discovery in data
• Nonmonotonic and uncertain reasoning
• Ontologies and description logics
• Probabilistic approaches of reasoning
• Syllogistic reasoning

Keynote Speaker

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.

Publication

The proceedings will be published in the CEUR Workshop Proceedings series (now available: CEUR Workshop Proceedings, Vol. 2194).

Workshop Organizers and Co-Chairs

Program Committee

Important Dates

Submission Details

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

Local information can be found on the web pages of the KI-2018 conference.

Last modified 2018-08-03