KI 2023 – Workshops and Tutorials

W1: AI-systems for the public interest

Organizers: Theresa Züger, Hadi Asghari

The number of AI projects aiming to serve the common good or a public interest is increasing rapidly. But often the information on these projects, their initiators, funders, methods and objectives is not transparent, hindering the goal of serving the public. Many AI applications touch upon sensitive areas with public wellbeing at stake, such as public health, mobility, and justice systems. In this interdisciplinary workshop we will connect public interest theory to the debate about AI projects and foster exchange amongst existing projects that use AI to serve the public interest to explore common challenges, methods, and standards.

Further information: https://www.hiig.de/en/call-for-papers-workshop-ki2023/ 

W2: 37th Workshop on (Constraint and Functional) Logic Programming (WLP 2023)

Organizers: Sibylle Schwarz, Mario Wenzel

The WLP provides a forum for exchanging ideas on declarative logic programming, non-monotonic reasoning, and knowledge representation, and facilitate interactions between research in theoretical foundations and in the design and implementation of logic-based programming systems. Contributions are welcome on all theoretical, experimental, and application aspects of logic, funtional, and constraint logic programming

Further information: https://dbs.informatik.uni-halle.de/wlp2023/ 

W3: 3rd Workshop on Humanities-Centred AI (CHAI 2023)

Organizers: Sylvia Melzer, Stefan Thiemann, Hagen Peukert

Inferring ancient cultural traditions from written artifacts, AI offers many opportunities to assist humanities scholars in their work. Both editorial projects and computer-aided evaluations such as text and data mining or linguistic analyses require the collection, storage, and linking of data in order to quickly identify core information of the written artefacts. Time-consuming procedures, such as the creation of dictionaries or the use of bibliographies through the automatic linking of data, enables the creation of extensive data sets and generation of additional information. Thus, AI supports humanities scholars to focus them more on their core task..

The workshop gathers AI researchers and interested humanities scholars.

Further information: https://www.csmc.uni-hamburg.de/ki2023-chai

W4: Deduktionstreffen (DT-2023)

Organizers: Claudia Schon, Florian Rabe

Automated Reasoning is a core field of artificial intelligence research with a focus on the development of models, procedures and software for the computer-assisted automation of logical reasoning. While automated reasoning is a quite broad and diverse field including many different types of formal systems, automated deduction specializes on the problem of correctly and efficiently deriving deductive conclusions from given assumptions, usually formulated in a formal logical language. Applications include, but are not limited to, automated theorem proving, software verification and synthesis, term rewrite systems, unification theory, planning and logic programming. The annual Workshop Deduktionstreffen (German, roughly translates to “deduction meeting”) is the prime activity of the Interest Group on Deduction Systems1 (Fachgruppe Deduktionssysteme) of the AI Chapter (Fachbereich KI) of the German Society of Informatics (Gesellschaft f¨ur Informatik, GI). Deduktionstreffen is a meeting with an informal and friendly atmosphere, where everyone (not only the German community) interested in deduction systems and related topics can report on their work in an accessible setting. A special focus of the workshop is on young researchers and students, who are particularly encouraged to present their ongoing research projects to a wider audience, and to receive constructive feedback from more experienced participants. Another goal of the meeting is to stimulate networking effects and to foster collaborative research projects.

Further information: https://fg-dedsys.gi.de/veranstaltung/deduktionstreffen-2023 

W5: 9th Workshop on Formal and Cognitive Reasoning (FCR-2023)

Organizers: Christoph Beierle, Kai Sauerwald, François Schwarzentruber, Frieder Stolzenburg

In real-life AI applications, information is usually pervaded by uncertainty and subject to change, and thus requires non-classical systems. 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 mechanisms has to be considered, such as analogical or defeasible reasoning, possibly in combination with machine learning methods. 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. A special focus is on papers that provide a base for connecting formal-logical models of knowledge representation and cognitive models of reasoning and learning, addressing formal and experimental or heuristic issues. We welcome papers on the following and any related topics: action and change; agents and multi-agent systems; analogical reasoning; argumentation theories; belief revision and belief update; cognitive modeling and empirical data; commonsense and defeasible reasoning; computational thinking; decision theory and preferences; inductive reasoning and cognition; knowledge representation in theory and practice; learning and knowledge discovery in data; machine learning and automated reasoning; nonmonotonic and uncertain reasoning; ontologies and description logics; probabilistic approaches of reasoning; syllogistic reasoning.

Further information: https://www.fernuni-hagen.de/wbs/fcr2023.html 

T1: Lets Talk about Palm Leafs - From Minimal Data to Text Understanding

Organizers: Magnus Bender, Marcel Gehrke, Tanya Braun

In recent years, large language models have greatly improved the state of the art for text understanding. However, large language models are often computationally expensive and work best in areas with huge amounts of training data. Unfortunately, there are areas where we do not have a lot of data available. For example, in digital humanities, we have researchers investigating poems that are written on palm leafs in old Tamil. They only have a few hundred or maybe a thousand poems (documents). In such a setting, using a general pre-trained large language model (there are no for old Tamil) and further training the model by subsampling from the corpus comes to its limits, given the limited data available. Nonetheless, a support in text understanding or information retrieval also has great value for these researchers. Therefore, in this tutorial, we give an overview of how different tasks can be performed with only minimal data available. We will use examples from the field of digital humanties to illustrate particular challenges.

Further information: https://www.uni-muenster.de/Informatik.AGBraun/en/research/tutorials/ki-23.html