Knowledge Representation and Reasoning

Welcome to the newly established group headed by Johannes (P.) Wallner, specializing on Knowledge Representation & Reasoning (KRR).

If you are interested in student topics (e.g., for a Bachelor's or Master's thesis) you can have look at the topics page. A Bachelor's or Master's thesis can be started anytime (contact).

Broadly construed, knowledge representation & reasoning in Artificial Intelligence is concerned with foundational research questions such as how to represent knowledge and how to reason based on knowledge. Our research and teaching focuses on

  • formal studies of prominent logic-based representations of knowledge, and
  • addressing challenging computational reasoning tasks arising in KRR.

Our research agenda is to further understanding of complex forms of reasoning in knowledge representation, and to bring promising approaches closer to application, by going from theory to practice.

One of our main current areas is computational argumentation. For a general introduction to the topic, you can have a look, e.g., at the Handbook of Formal Argumentation or this article

** New course **: Logic-based Knowledge Representation (winter term).

Below you find recent news (news archive).

Recent News

Participation in Competition


Our group participated in the recent 5th International Competition on Computational Models of Argumentation (ICCMA). In this competition solvers for computationally complex reasoning tasks in argumentation are evaluated against each other in terms of runtime efficiency.

Our group participated in two tracks: the dynamic track and the ABA track, with the former focusing on dynamically changing argumentation scenarios and the latter incorporating structural aspects of arguments. We contributed to the following submissions:

  • k-Solutions, developed by Christian Pasero,
  • ASTRA, developed by Andrei Popescu,
  • AcbAr, developed by Tuomo Lehtonen, and 
  • ASPforABA, developed by Tuomo Lehtonen.

These approaches each present their own algorithmic approach to computationally complex reasoning tasks. ASPforABA scored the first place where it participated. The results were presented in this year's International Conference on Principles of Knowledge Representation and Reasoning (KR2023) in Rhodes. Detailed results can be found here.

We thank the developers, contributors, and the competition organizers for the invaluable work for the scientific community!

Paper accepted to FCR'23


Our work on "Ranking-based Semantics for Assumption-based Argumentation" was accepted to the 9th Workshop on Formal and Cognitive Reasoning (FCR'23), a workshop at the German KI conference in 2023. 

In this work we look at ways of defining ranking-based semantics on the knowledge representation formalism assumption-based argumentation (ABA). Our ranking-based semantics relate assumptions, and we present ways of obtaining such semantics from existing ranking-based semantics on abstract argumentation. We define principles, i.e., properties of interest, ranking-based semantics on ABA may satisfy. 

Participation in AI Summer School 2023


Our group participated in the AI Summer School 2023, organized by the Austrian Society for Artificial Intelligence (ASAI) in Vienna. The pogramme included aspects of machine learning, knowledge graphs, declarative problem solving, and ethics in AI. 

Paper accepted to JELIA'23


Our work on "Reasoning in Assumption-based Argumentation using Tree-decompositions" was accepted to JELIA, the european conference on logics in Artificial Intelligence. 

In this work we look at methods that decompose instances of complex reasoning tasks arising in formal argumentation. We decompose the structures into tree-decompositions and apply a recently developed declarative framework for specifying dynamic programming algorithms on such tree-decompositions.

Dragon Boat University Cup


We participated in this year's edition of the Dragon Boat University Cup with the boat of our faculty ("Die Passionierten"). We placed eighth in the ranking and are looking forward to next year!

Two papers accepted to KR'23


Our works on "Argumentation Frameworks induced by Assumption-based Argumentation: Relating Size and Complexity" and "Argumentative Reasoning in ASPIC+ under Incomplete Information" were accepted to KR'23, a leading conference on knowledge representation and reasoning.

In the first paper we study reasoning in the formal approach of assumption-based argumentation (ABA). After observing that argumentative reasoning in this instance has two sources of complexity, namely construction of arguments and complexity of reasoning based on such arguments, we identify and study fragments of ABA that can be utilized to confine complexity to either source. In an experimental evaluation one of the fragments, that of of so-called atomic ABAs, shows promise for utilization in systems that solve ABA reasoning.

In the second work we look at ASPIC+, an argumentation formalisms related to ABA, and investigate reasoning under incomplete information. In particular, we look at notions of stability and relevance, with stability intuitively signaling when a justification status of a conclusion cannot be altered with new information and relevance points towards useful information to achieve such a stable status. We study the complexity of these problems, provide algorithmic approaches, and an empirical evaluation of our algorithms.