Research Lab "Algorithms for Abstract Argumentation"[go to overview]
Summer Term 2021
The research field Knowledge Representation is a branch of Artificial Intelligence that deals with the logical formalization of information and inference processes. Formal models of argumentation  are a relatively new and promising approach to knowledge representation. This approach is based on the representation of arguments (i.e., plausible chains of inference for specific conclusions) and interaction between such arguments. Formal models of argumentation are based on human every-day reasoning. They are analytically appealing and therefore form a popular approach to model commonsense reasoning in artificial intelligence. A particular important approach in formal argumentation is abstract argumentation  that abstracts from the inner structure of arguments. In abstract argumentation frameworks, arguments are represented as vertices in a directed graph and a directed edge is interpreted as an attack of one argument to another. Reasoning with abstract argumentation frameworks involves the computation of extensions, i.e., sets of arguments that can jointly be accepted. A semantics describes a general recipe to determine extensions given a certain abstract argumentation framework. Many such semantics have been proposed for abstract argumentation  and most of them are computationally hard .
Interest in algorithms for solving reasoning problems in abstract argumentation has increased recently, also fostered by the biannual International Competition of Computational Models of Argumentation (ICCMA), see also  for a survey. In this research lab, you will contribute to this field by developing computational approaches to solve some selected reasoning problems. While a majority of the successful solvers rely on out-of-the-box SAT solving technology to solve (sub-)tasks, this research lab will explore direct approaches . More precisely, this research lab will transfer the conflict-driven clause learning (CDCL) approach  directly to abstract argumentation and apply it to the selected reasoning problems. It is desirable that further modern techniques from SAT solving technology will be exploited as well.
If you would like to participate in the seminar, please register with PD Dr. Matthias Thimm by sending an informal e-mail. The deadline for this is February 23, 2021. A kick-off meeting will take place on **February 24, 2021, at 14:00 online in BigBlueButton**. The participation in the kick-off meeting is **mandatory** if you wish to take part in the research lab. The research lab is targeted at master students of the computer science department of any study programme. Interest in symbolic approaches to artificial intelligence and experience in programming (in particular in low-level programming languages such as C/C++) is required.
- K. Atkinson, P. Baroni, M. Giacomin, A. Hunter, H. Prakken, C. Reed, G. R. Simari, M. Thimm, and S. Villata. Toward artificial argumentation. AI Magazine, 38(3):25–36, 2017.
- Phan Minh Dung. On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games. Artificial Intelligence, 77(2):321–358, 1995.
- Pietro Baroni, Martin Caminada, Massimiliano Giacomin. Abstract Argumentation Frameworks and Their Semantics. In Handbook of Formal Argumentation. College Publications, 2018.
- Wolfgang Dvorak and Paul E. Dunne. Computational Problems in Formal Argumentation and their Complexity. In Handbook of Formal Argumentation. College Publications, 2018.
- Federico Cerutti, Sarah A. Gaggl, Matthias Thimm, Johannes P. Wallner. Foundations of Implementations for Formal Argumentation. In Handbook of Formal Argumentation. College Publications, 2018.
- Joao Marques-Silva, Ines Lynce and Sharad Malik. CDCL Solvers. In Handbook of Satisfiability. IOS Press, 2009. </ol>