Knowledge Representation and Reasoning

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Welcome to the newly established Knowledge Representation & Reasoning (KRR) group.

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

Below you find recent news (news archive).

Recent News

Article accepted to Artificial Intelligence

Our paper "Advanced Algorithms for Abstract Dialectical Frameworks based on Complexity Analysis of Subclasses and SAT Solving" was accepted to the Artificial Intelligence journal. In this article we investigate computational complexity, develop algorithms, and present a prototypical implementation, for Abstract Dialectical Frameworks (ADFs), which are a formalization of arguments and their relationships within the area of Computational Argumentation. We study several fragments of the formal language of ADFs and show complexity results for these fragments. Based on these insights, we develop algorithmic approaches to solve reasoning tasks on ADFs, and present an empirical evaluation of resulting prototypical implementations. More information in pure

PhD student positions

We have two openings for PhD students on the topic of knowledge representation and reasoning. See Jobs.

Paper accepted to AAAI'22

Our paper "An Axiomatic Approach to Revising Preferences" was accepted to AAAI 2022. In this paper we study a general approach to revision of preferences, along the lines of the well-known AGM approach to (belief) revision in AI, which bases revision operations on rationality postulates (formal properties stating desirable or interesting behavior). More information in pure.

New project grant awarded by FWF

The Austrian Science Fund (FWF) awarded a new research project grant to Wallner as the principle investigator (PI). The project is planned for three years, and provides foundational research for computational models of argumentation within the larger area of Artificial Intelligence.

Chapter in Handbook of Formal Argumentation

Wallner contributed to a chapter of the recent Handbook of Formal Argumentation, second volume. This handbook is an effort of the research community on Computational Argumentation to provide a thorough overview of major strands of the field. More information in pure.

Paper accepted to TPLP

In the paper "Harnessing Incremental ASP Solving for Reasoning in Assumption-Based Argumentation" we develop novel algorithms based on counterexample-guided abstraction refinement and incremental answer set programming (ASP) to address challenging computational problems in assumption-based argumentation. More information in pure.