DrNico Potyka

I am a research associate in the Computational Logic and Argumentation group working on the ADIX (Argumentation-based Deep Interactive eXplanations) project. My research interests include

Argumentation
Explainable AI
Hybrid AI/ Neuro-symbolic AI
Uncertain Reasoning
Inconsistency Tolerance
Knowledge Graphs
Applied Optimization
Algorithm engineering and analysis
Before joining Imperial in 2022, I had previous academic positions at

the Analytic Computing group at the University of Stuttgart (Research & Teaching, 2020-2022),
the Artificial Intelligence group at the University of Osnabrück (Research & Teaching, 2016-2020),
the Knowledge-based Systems group at the University of Hagen (PhD student & Research assistant, 2011-2015)
and spent research visits at

the Automata Theory group at the University of Dresden (3/2019),
the Intelligent Systems group at University College London (2/2017 and 3/2018),
the Artificial Intelligence group at the University of Mannheim (12/2015),
the Research Centre for Knowledge and Data at the University of Bolzano (11/2015).
Since 2016, I gave more than 20 courses in the area of Artificial Intelligence. My teaching portfolio includes courses on

Artificial Intelligence (Introduction and Advanced),
Probabilistic Reasoning,
Nature-inspired Algorithms,
Multiagent Systems,
Knowledge Graphs,
Time Series Analysis and Forecasting.
Before studying Computer Science (2005-2010), I worked in Electronics (2000-2001) and Logistics (2001-2004). After finishing my MSc, I worked as a software engineer on Enterprise resource planning (2010-2011) before starting my PhD.

Software

Attractor: The Attractor library allows modeling and solving Quantitative/Gradual Argumentation problems. Arguments can be modeled in a directed graph, where nodes represent arguments and edges attack or support relationships between them. Every argument has an initial weight that represent an apriori strength when all other arguments are ignored. Reasoning algorithms assign a final strength to every argument based on its initial weight and the final strength of its attackers and supporters. Attractor supports various semantics and computes the final strength values by viewing the reasoning problem as a dynamical system that can be solved by numerical methods in polynomial-time.

Corresponding publication

Nico Potyka:Extending Modular Semantics for Bipolar Weighted Argumentation.International Conference on Autonomous Agents and MultiAgent System (AAMAS 2019): 1722-1730

ProBabble: ProBabble allows modeling and reasoning about probabilistic argumentation problems. Similar to Gradual Argumentation Frameworks, argumentation problems are described by directed graphs. However, relationships between arguments are described by probabilistic constraints. ProBabble supports linear atomic constraints that allow representing many interesting relationships and reasoning in polynomial-time.

Corresponding publication

Nico Potyka:A Polynomial-time Fragment of Epistemic Probabilistic Argumentation.International Journal of Approximate Reasoning. 115: 265-289 (2019)

Log4KR: Log4KR is a Java library that allows modeling and reasoning about probabilistic reasoning problems. It supports propositional and relational probabilistic logic including probabilistic conditional logics. In order to deal with inconsistencies, it also provides implementations of inconsistency measures and paraconsistent reasoning algorithms. The library can be downloaded as part of the KReator project.

Corresponding publication

Nico Potyka, Matthias Thimm:Inconsistency-tolerant Reasoning over Linear Probabilistic Knowledge Bases.International Journal of Approximate Reasoning. 88: 209-236 (2017)