Nico Potyka

Nico Potyka
Imperial College London | Imperial · Department of Computing

Dr. rer. nat.

About

69
Publications
5,060
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301
Citations
Introduction
My general research interest is in scalable and interpretable approaches to Artificial Intelligence. My current work focuses on foundations, algorithms and applications of abstract argumentation and knowledge graphs.
Additional affiliations
January 2022 - present
Imperial College London
Position
  • Research Associate
Description
  • Working on argumentation technology for explainable AI.
April 2020 - December 2021
Universität Stuttgart
Position
  • PostDoc Position
April 2019 - April 2019
Technische Universität Dresden
Position
  • Researcher
Description
  • Discussion of probabilistic and concept reasoning approaches.
Education
May 2011 - December 2015
FernUniversität in Hagen
Field of study
  • Computer Science
October 2008 - November 2010
FernUniversität in Hagen
Field of study
  • Computer Science
September 2005 - August 2008
Ostfalia Hochschule für angewandte Wissenschaften
Field of study
  • Computer Science

Publications

Publications (69)
Conference Paper
Full-text available
Weighted bipolar argumentation frameworks determine the strength of arguments based on an initial weight and the strength of their attackers and supporters. These frameworks can be applied to model and solve problems that arise in areas like social media analysis and decision support. Approaches for computing strength values often assume an acyclic...
Conference Paper
Full-text available
Epistemic graphs are a recent generalization of epistemic probabilistic argumentation. Relations between arguments can be supporting, attacking, as well as neither supporting nor attacking. These interdependencies are represented by epistemic constraints, and the semantics of epistemic graphs are given in terms of probability distributions satisfyi...
Preprint
Full-text available
Weighted bipolar argumentation frameworks allow model-ing decision problems and online discussions by defining arguments and their relationships. The strength of arguments can be computed based on an initial weight and the strength of attacking and supporting arguments. Application domains include social media analysis and decision support. While p...
Article
Full-text available
We consider the problem of reasoning under uncertainty in the presence of inconsistencies. Our knowledge bases consist of linear probabilistic constraints that, in particular, generalize many probabilistic-logical knowledge representation formalisms. We first generalize classical probabilistic models to inconsistent knowledge bases by considering a...
Preprint
Full-text available
There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit faithfulness in the context of continuous data and propose two formal definitions of faithfulness for feature attribution methods. Qualitative faithfulness demands that score...
Preprint
Full-text available
Recently, various methods for representation learning on Knowledge Bases (KBs) have been developed. However, these approaches either only focus on learning the embeddings of the data-level knowledge (ABox) or exhibit inherent limitations when dealing with the concept-level knowledge (TBox), e.g., not properly modelling the structure of the logical...
Conference Paper
Full-text available
One natural approach to probabilistic logic programming is Nilsson's probabilistic logic. We discuss the basic framework in the propositional setting, some reasoning ideas and extensions that allow handling inconsistent information. We then discuss its relationship to probabilistic epistemic argumentation. Roughly speaking, probabilistic epistemic...
Chapter
Full-text available
Computational models of argumentation are an interesting tool to represent decision processes. Bipolar abstract argumentation studies the question of which arguments a rational agent can accept given attack and support relationships between them. We present a generalization of the fundamental complete semantics from attack-only graphs to bipolar gr...
Chapter
Full-text available
Argumentation is inherently pervaded by uncertainty, which can arise as a result of the context in which argumentation is used, the kinds of agents that are involved in a given situation, the types of arguments that are used, and more. One of the prominent approaches for handling uncertainty in argumentation is probabilistic argumentation, which of...
Preprint
Full-text available
Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interesting tool for interpretable machine learning. It has been noted recently that their mechanics are closely related to neural networks, which allows learning their weights...
Preprint
Full-text available
Graph Convolutional Networks (GCNs) are typically studied through the lens of Euclidean geometry. Non-Euclidean Riemannian manifolds provide specific inductive biases for embedding hierarchical or spherical data, but cannot align well with data of mixed topologies. We consider a larger class of semi-Riemannian manifolds with indefinite metric that...
Article
We show that an interesting class of feed-forward neural networks can be understood as quantitative argumentation frameworks. This connection creates a bridge between research in Formal Argumentation and Machine Learning. We generalize the semantics of feed-forward neural networks to acyclic graphs and study the resulting computational and semantic...
Preprint
Full-text available
We show that an interesting class of feed-forward neural networks can be understood as quantitative argumentation frameworks. This connection creates a bridge between research in Formal Argumentation and Machine Learning. We generalize the semantics of feed-forward neural networks to acyclic graphs and study the resulting computational and semantic...
Conference Paper
Full-text available
Argumentation Frameworks represent arguments and their relationships like attack and support in a graph. Their simple structure makes them easily interpretable and therefore a potentially interesting tool for explainable machine learning. We discuss some ideas for modeling and solving classification problems as abstract argumentation problems. As o...
Conference Paper
Full-text available
Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence, non-classical reasoning approaches are required. Here, we investigate probabilistic epistemic argumentation as a tool for...
Preprint
Full-text available
Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence, non-classical reasoning approaches are required. Here, we investigate probabilistic epistemic argumentation as a tool for...
Conference Paper
Full-text available
Bipolar abstract argumentation frameworks allow modeling decision problems by defining pro and contra arguments and their relationships. In some popular bipolar frameworks, there is an inherent tendency to favor either attack or support relationships. However, for some applications, it seems sensible to treat attack and support equally. Roughly spe...
Conference Paper
Full-text available
We explain how abstract argumentation problems can be encoded as Markov networks. From a computational perspective, this allows reducing argumentation tasks like finding labellings or deciding credulous and sceptical acceptance to probabilistic inference tasks in Markov networks. From a semantical perspective, the resulting probabilistic argumentat...
Chapter
Full-text available
Gradual argumentation frameworks allow modeling arguments and their relationships and have been applied to problems like decision support and social media analysis. Semantics assign strength values to arguments based on an initial belief and their relationships. The final assignment should usually satisfy some common-sense properties. One property...
Article
Full-text available
Probabilistic argumentation allows reasoning about argumentation problems in a way that is well-founded by probability theory. However, in practice, this approach can be severely limited by the fact that probabilities are defined by adding an exponential number of terms. We show that this exponential blowup can be avoided in an interesting fragment...
Conference Paper
Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by probability functions over possible worlds and can be adjusted to new beliefs using update operators. While the use of probability functions puts this approach on a solid fo...
Chapter
Full-text available
Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by probability functions over possible worlds and can be adjusted to new beliefs using update operators. While the use of probability functions puts this approach on a solid fo...
Chapter
Full-text available
This extended abstract summarizes the key results from [10].
Article
In an epistemic graph, belief in arguments is represented by probability distributions. Furthermore, the influence that belief in arguments can have on the belief in other arguments is represented by constraints on the probability distributions. Different agents may choose different constraints to describe their reasoning, thus making epistemic gra...
Preprint
Full-text available
Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by probability functions over possible worlds and can be adjusted to new beliefs using update operators. While the use of probability functions puts this approach on a solid fo...
Preprint
Full-text available
Weighted bipolar argumentation frameworks offer a tool for decision support and social media analysis. Arguments are evaluated by an iterative procedure that takes initial weights and attack and support relations into account. Until recently, convergence of these iterative procedures was not very well understood in cyclic graphs. Mossakowski and Ne...
Preprint
Full-text available
Probabilistic argumentation allows reasoning about argumentation problems in a way that is well-founded by probability theory. However, in practice, this approach can be severely limited by the fact that probabilities are defined by adding an exponential number of terms. We show that this exponential blowup can be avoided in an interesting fragment...
Conference Paper
Full-text available
When combining beliefs from different sources, often not only new knowledge but also conflicts arise. In this paper, we investigate how we can measure the disagreement among sources. We start our investigation with disagreement measures that can be induced from inconsistency measures in an automated way. After discussing some problems with this...
Preprint
Weighted bipolar argumentation frameworks determine the strength of arguments based on an initial weight and the strength of their attackers and supporters. They find applications in decision support and social media analysis. Mossakowski and Neuhaus recently introduced a unification of different models and gave sufficient conditions for convergenc...
Code
Attractor is a Java library that can be used to solve weighted bipolar argumentation problems with continuous dynamical systems. Weighted bipolar argumentation frameworks are an AI formalism that allow modeling decision problems and online discussions by defining arguments and their relationships. The strength of arguments can be computed based...
Data
This archive contains 3,000 randomly generated bipolar argumentation graphs (BAGs) ranging from size 100 to 3,000. Warning: the archive is 158 MB large and the uncompressed size is 987 MB. The random generator is described in Potyka, N. Continuous Dynamical Systems for Weighted Bipolar Argumentation. In 16th International Conference on Princip...
Conference Paper
Full-text available
We present a probabilistic extension of the description logic ALC for reasoning about statistical knowledge. We consider conditional statements over proportions of the domain and are interested in the probabilistic-logical consequences of these proportions. After introducing some general reasoning problems and analyzing their properties, we present...
Conference Paper
Full-text available
In persuasion dialogues, the ability of the persuader to model the per-suadee allows the persuader to make better choices of move. The epistemic approach to probabilistic argumentation is a promising way of modelling the per-suadee's belief in arguments, and proposals have been made for update methods that specify how these beliefs can be updated a...
Technical Report
Full-text available
We present a probabilistic extension of the description logic ALC for reasoning about statistical knowledge. We consider conditional statements over proportions of the domain and are interested in the probabilistic-logical consequences of these proportions. After introducing some general reasoning problems and analyzing their properties, we present...
Technical Report
Full-text available
In persuasion dialogues, the ability of the persuader to model the persuadee allows the persuader to make better choices of move. The epistemic approach to probabilistic argumentation is a promising way of modelling the persuadee's belief in arguments, and proposals have been made for update methods that specify how these beliefs can be updated at...
Conference Paper
Full-text available
A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic \(\mathcal {ALCP}\) that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. \(\mathcal {ALCP}\) allows the expression of logical depen...
Conference Paper
Full-text available
We propose a novel framework for computational concept invention. As opposed to recent implementations of Fauconnier’s and Turner’s Conceptual Blending Theory, our framework simplifies computational concept invention by focusing on concepts’ functions rather than on structural similarity of concept descriptions. Even though creating an optimal comb...
Conference Paper
Full-text available
We propose a probabilistic-logical framework for group decision-making. Its main characteristic is that we derive group preferences from agents' beliefs and utilities rather than from their individual preferences as done in social choice approaches. This can be more appropriate when the individual preferences hide too much of the individuals' opini...
Article
Full-text available
A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic ALCP that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. ALCP allows the expression of logical dependencies on the domain and prob...
Article
Full-text available
The expert system shell MECore provides a series of knowledge management operations to define probabilistic knowledge bases and to reason under uncertainty. To provide a reference work for MECore algorithmics, we bring together results from different sources that have been applied in MECore and explain their intuitive ideas. Additionally, we report...
Thesis
Full-text available
Classical logic can be regarded as the study of drawing deductive conclusions from consistent assumptions. However, the classical truth values true and false are often insufficient for applications in uncertain domains. Probabilistic logics overcome this problem by interpreting formulas by probabilities, where the probability 1 corresponds to true...
Chapter
Full-text available
We investigate the relationships between some relational probabilistic conditional logics by comparing their semantics. In order to do so, we will order the different semantics with respect to their strength. Subsequently, we will provide several results that allow drawing conclusions from reasoning results under particular semantics about the resu...
Conference Paper
LabSAT is a software system that for a giving abstract argumentation system AF can determine some or all extensions, and can decide whether an argument is credulously or sceptically accepted. These tasks are solved for complete, stable, preferred, and grounded semantics. LabSAT’s implementation employs recent results on the connection between argum...
Conference Paper
Full-text available
We consider the problem of reasoning over probabilistic knowledge bases with different priority levels. While we assume that the knowledge is consistent on each level, there can be inconsistencies between different levels. Examples arise naturally in hierarchical domains when general knowledge is overwritten with more specific information. We exten...
Conference Paper
Full-text available
The classical probabilistic entailment problem is to determine upper and lower bounds on the probability of formulas, given a consistent set of probabilistic assertions. We generalize this problem by omitting the consistency assumption and, thus, provide a general framework for probabilistic reasoning under inconsistency. To do so, we utilize incon...
Conference Paper
A knowledge base in the logic FO-PCL is a set of relational probabilistic conditionals. The models of such a knowledge base are probability distributions over possible worlds, and the principle of Maximum Entropy (ME) selects the unique model having maximum entropy. While previous work on FO-PCL focused on ME model computation, in this paper we pro...
Article
Combining logic with probability theory provides a solid ground for the representation of and the reasoning with uncertain knowledge. Given a set of probabilistic conditionals like “If A then B with probability x”, a crucial question is how to extend this explicit knowledge, thereby avoiding any unnecessary bias. The connection between such probabi...
Data
This archive contains java code for the examples from Nico Potyka, Matthias Thimm Probabilistic Reasoning with Inconsistent Beliefs using Inconsistency Measures IJCAI 2015. We make use of the libraries ANTLR http://www.antlr.org/ BSD license (see below) and Oj!Algorithms http://ojalgo.org/ MIT license (see below). The BSD license Redistri...
Article
Full-text available
Coping with uncertain knowledge and changing beliefs is essential for reasoning in dynamic environments. We generalize an approach to adjust probabilistic belief states by use of the relative entropy in a propositional setting to relational languages, leading to a concept for the evolution of relational probabilistic belief states. As a second cont...
Conference Paper
Full-text available
Consolidation describes the operation of restoring consistency in an inconsistent knowledge base. Here we consider this problem in the context of probabilistic conditional logic, a language that focuses on probabilistic conditionals (if-then rules). If a knowledge base, i. e., a set of probabilistic conditionals, is inconsistent traditional model-b...
Conference Paper
Full-text available
Inconsistency measures help analyzing contradictory knowledge bases and resolving inconsistencies. In recent years several measures with desirable properties have been proposed, but often these measures correspond to combinatorial or non-convex optimization problems that are hard to solve in practice. In this paper, I study a new family of inconsis...
Conference Paper
Full-text available
Coping with uncertain knowledge and changing beliefs is essential for reasoning in dynamic environments. We generalize an approach to adjust probabilistic belief states by use of the relative entropy in a propositional setting to relational languages. As a second contribution of this paper, we present a method to compute such belief changes by cons...
Conference Paper
The expert system shell MECore provides a series of knowledge management operations to define probabilistic knowledge bases and to reason under uncertainty. We report on our ongoing work regarding further development of MECore's algorithms to compute optimum entropy distributions. We provide some intuition for these methods and point out their bene...
Conference Paper
We present a case-study of applying probabilistic logic to the analysis of clinical patient data in neurosurgery. Probabilistic conditionals are used to build a knowledge base for modelling and representing clinical brain tumor data and expert knowledge of physicians working in this area. The semantics of a knowledge base consisting of probabilisti...
Conference Paper
By using the principle of maximum entropy incomplete probabilistic knowledge can be completed to a full joint distribution. This inductive knowledge representation method can be reversed to extract probabilistic rules from an empirical probability distribution. Based on this idea propositional learning approach has been developed. Recently, an exte...
Conference Paper
Dealing with uncertainty that is inherently present in any medical domain, is one of the major challenges when designing a medical decision support system. We demonstrate how probabilistic logic can be used to design medical knowledge bases at the example of analysing clinical brain tumor data. We use MECoRe, a system implementing probabilistic con...
Conference Paper
Probabilistic conditional logics offer a rich and well-founded framework for designing expert systems. The factorization of their maximum entropy models has several interesting applications. In this paper a general factorization is derived providing a more rigorous proof than in previous work. It yields an approach to extend Iterative Scaling varia...
Conference Paper
The principle of maximum entropy inductively completes the knowledge given by a knowledge base R, and it has been suggested to view learning as an operation being inverse to inductive knowledge completion. While a corresponding learning approach has been developed when R is based on propositional logic, in this paper we describe an extension to a r...
Conference Paper
A possible approach to extend classical logics to probabilistic logics is to consider a probability distribution over the classical interpretations that satisfies some constraints and maximizes entropy. Over the past years miscellaneous languages and semantics have been considered often based on similar ideas. In this paper a hierarchy of general p...
Conference Paper
In a knowledge representation sense, the principle of maximum entropy (ME) states that a set of probabilistic rules is best represented by that probability distribution satisfying all rules and possessing maximum entropy. Accordingly an ME-inference function takes a set of probabilistic rules and determines the ME-optimal distribution representing...
Conference Paper
Full-text available
We present a learning scenario for relational learning which takes statistics on a population as well as uncertainty on individuals into account. It can be considered as an extension of a propositional maximum entropy (ME) framework which regards learning as the inverse process to inference. We start with the introduction of relational probabilisti...

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Projects

Projects (5)
Project
Es werden in 8 verschiedenen Projekten... ...Mechanismen des menschlichen Vergessens für einen technischen und organisationalen Kontext nutzbar gemacht. ...existierende Organisationstheorien weiterentwickelt im Hinblick auf die Frage, wie eine Passung zwischen den Prozessen, MitarbeiterInnen, Technologien und Strukturen der Organisation und der Informationsmenge, die intern und extern der Organisation generiert wird, hergestellt werden kann, in dem die Informationssysteme in Organisationen befähigt werden zu vergessen. ...interdisziplinär neue Methoden und Übertragungswege für Organisationen entwickelt, um die Balance zwischen stetig wachsenden Mengen an gespeicherten Informationen und einer Informationsreduktion durch Vergessen zu finden. Beteiligte Forschung: https://www.researchgate.net/project/Intentionales-Vergessen-von-arbeitsteiligen-geschaeftsprozessbezogenen-Aktivitaeten-in-der-Fertigung https://www.researchgate.net/project/AdaptPRO-Adaptive-Process-and-Role-Design-in-Organizations https://www.researchgate.net/project/Cyber-physical-forgetting-in-socio-digital-systems https://www.researchgate.net/project/Intentional-Forgetting-of-Everyday-Work-Behavior https://www.researchgate.net/project/EVOWIPE-Explizites-Vergessen-ontologiebasierten-Wissens-in-der-Produktentwicklung https://www.researchgate.net/project/Intentional-Forgetting-within-Organizations https://www.researchgate.net/project/Getrost-vergessen https://www.researchgate.net/project/Intentional-Forgetting