Odekerken, D., (2023). Argumentative reasoning with incomplete information in law enforcement Online Handbook of Argumentation for AI
In my dissertation research, I design and study argumentation-based systems for reasoning with incomplete nformation. These systems are able to construct an initial dvice on some topic (justification); investigate the possibility that additional, yet uncertain, information can change the conclusion (stability) and if so, which information is worth investigating (relevance). The systems’ requirements of detecting justification, stability and relevance correspond to theoretical problems in computational argumentation. Most of these problems are in high complexity classes, but can still be solved in reasonable time using efficient (approximation) algorithms. In this paper, I discuss how these algorithms can be used in four practical applications in law enforcement.

Odekerken, D., Bex, F., & Prakken, H. (2023). Precedent-based reasoning with incomplete cases To be presented at JURIX 2023
We extend the result model for precedent-based reasoning with incomplete case bases. In contrast to regular case bases, these consist of incomplete cases for which not all dimension values need to be specified, but rather each dimension is assigned a set of possible values. The outcome of cases then applies for each (combination of) the possible dimension values. Building on earlier proposed notions of justification and stability for incomplete focus cases, we introduce the notion of possible justification statuses, which are required to maintain consistency of the incomplete case base. We demonstrate how these theoretic notions can be applied in practice for human-in-the-loop decision support, discuss their computational complexity and provide efficient algorithms.

Odekerken, D., Borg, A., Bex, F. (2023). Justification, Stability and Relevance in Incomplete Argumentation Frameworks Argument & Computation
We explore the computational complexity of justification, stability and relevance in incomplete argumentation frameworks (IAFs). IAFs are abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. These IAFs can be completed by deciding for each uncertain argument or attack whether it is present or absent. Such a completion is an abstract argumentation framework, for which it can be decided which arguments are acceptable under a given semantics. The justification status of an argument in a completion then expresses whether the argument is accepted (IN), not accepted because it is attacked by an accepted argument (OUT) or neither (UNDEC). For a given IAF and certain argument, the justification status of that argument need not be the same in all completions. This is the issue of stability, where an argument is stable if its justification status is the same in all completions. For arguments that are not stable in an IAF, the relevance problem is of interest: which uncertain arguments or attacks should be investigated for the argument to become stable? In this paper, we define justification, stability and relevance for IAFs and provide a complexity analysis for these problems under grounded, complete, preferred and stable semantics.

Odekerken, D., Borg, A., & Berthold, M. (2023). Demonstrating PyArg 2.0 To be presented at the 7th Workshop on Advances in Argumentation in Artificial Intelligence
We demonstrate the latest release of PyArg, an open-source Python package of implementation algorithms with a web interface. PyArg provides various argumentation-based functionalities, including evaluation and visualisation of abstract argumentation frameworks, ASPIC+ argumentation theories and assumption-based argumentation frameworks; explanation algorithms; multiple generators; a learning environment; implementations of theoretical papers and a showcase of a practical application.

Source code Visual interface Documentation website

Odekerken, D., Lehtonen, T., Borg, A., Wallner, J.P. & Järvisalo, M. (2023). Argumentative Reasoning in ASPIC+ under Incomplete Information Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning
Reasoning under incomplete information is an important research direction in AI argumentation. Most computational advances in this direction have so-far focused on abstract argumentation frameworks. Development of computational approaches to reasoning under incomplete information in structured formalisms remains to-date to a large extent a challenge. We address this challenge by studying the so-called stability and relevance problems---with the aim of analyzing aspects of resilience of acceptance statuses in light of new information---in the central structured formalism of ASPIC+. Focusing on the case of the grounded semantics and an ASPIC+ fragment motivated through application scenarios, we develop exact ASP-based algorithms for stability and relevance in incomplete ASPIC+ theories, and pinpoint the complexity of reasoning about stability (coNP-complete) and relevance (Sigma_2^P-complete), further justifying our ASP-based approaches. Empirically, the algorithms exhibit promising scalability, outperforming even a recent inexact approach to stability, with our ASP-based iterative approach being the first algorithm proposed for reasoning about relevance in ASPIC+.

Source code Supplement

Odekerken, D., Borg, A. & Berthold, M. (2023). Accessible Algorithms for Applied Argumentation First International Workshop on Argumentation and Applications
Computational argumentation is a promising research area, yet there is a gap between theoretical contributions and practical applications. Bridging this gap could potentially raise interest in this topic even more. We argue that one part of the bridge could be an open-source package of implementations of argumentation algorithms, visualised in a web interface. Therefore we present a new release of PyArg, providing various new argumentation-based functionalities -- including multiple generators, a learning environment, implementations of theoretical papers and a showcase of a practical application -- in a new interface with improved accessibility.

Source code Visual interface Documentation website

Odekerken, D., Bex, F., & Prakken, H. (2023). Justification, Stability and Relevance for Case-based Reasoning with Incomplete Focus Cases. Nineteenth International Conference for Artificial Intelligence and Law
We define and study the notions of stability and relevance for precedent-based reasoning, focusing on Horty's result model of precedential constraint. According to this model, precedents constrain the possible outcomes for a focus case, which is a yet undecided case, where precedents and the focus case are compared on their characteristics (called dimensions). In this paper, we refer to the enforced outcome for the focus case as its justification status. In contrast to earlier work, we do not assume that all dimension values of the focus case have been established with certainty: rather, each dimension has a set of possible value assignments. We define a focus case as stable if its justification status is the same for every choice of the possible value assignments. For focus cases that are not stable, we study the task of identifying relevance: which possible value assignments should be excluded to make the focus case stable? We show how the tasks of identifying justification, stability and relevance can be exploited for human-in-the-loop decision support. Finally, we discuss the computational complexity of these tasks and provide efficient algorithms.

Implementations and demo Extended version with full proofs

Winner of the Donald Berman Best Student Paper Award.

Berthold, M., Knorr, M., & Odekerken, D. (2023). Forgetting Web. 39th International Conference on Logic Programming (Technical Communications)
The relatively young area of forgetting is concerned with the removal of selective information, while preserving other knowledge. This might be useful or even necessary, for example, to simplify a knowledge base or to tend legal requests. In the last few years, there has been an ample amount of research in the field, in particular with respect to logic programs, spanning from theoretical considerations to more practical applications, starting at the conceptual proposal of forgetting, to suggestions of properties that should be satisfied, followed by characterizations of abstract classes of operators that satisfy these properties, and finally the definition of concrete forgetting procedures. In this work we present novel Python implementations of all the forgetting procedures that have been proposed to date on logic programs. We provide them in a web interface, and hope to thereby give anybody who is interested a low-barrier overview of the landscape.

Source code Visual interface

Odekerken, D. (2022). Justification, Stability and Relevance for Transparent and Efficient Human-in-the-Loop Decision Support. In Online Handbook of Argumentation for AI, Vol. 3
One of the promises of artificial intelligence is improving efficiency in various processes, including decision-making. For specific decisions it is vital that human experts understand and are able to influence machine-made advice. In my dissertation research, I design and study argumentation-based systems for transparent human-in-the-loop decision support. Based on a domain-specific argumentation setting, these systems are able to construct an initial advice on some decision (justification); investigate the possibility that additional, yet uncertain, information can change the conclusion (stability) and if so, which information is worth investigating (relevance). The systems’ requirements of detecting justification, stability and relevance correspond to theoretical problems in computational argumentation, most of which are in high complexity classes. In order to achieve reasonable estimations for these problems in polynomial time, I develop and investigate not only exact algorithms but also approximations.

Odekerken, D., Borg, A., & Bex, F. (2022). Stability and Relevance in Incomplete Argumentation Frameworks. In Computational Models of Argument
We explore the computational complexity of stability and relevance in incomplete argumentation frameworks (IAFs), abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. IAFs can be specified by, e.g., making uncertain arguments or attacks certain; the justification status of arguments in an IAF is determined on the basis of the certain arguments and attacks. An argument is stable if its justification status is the same in all specifications of the IAF. For arguments that are not stable in an IAF, the relevance problem is of interest: which uncertain arguments or attacks should be investigated for the argument to become stable? We redefine stability and define relevance for IAFs and study their complexity.

Borg, A., & Odekerken, D. (2022). PyArg for Solving and Explaining Argumentation in Python: Demonstration. In Computational Models of Argument
We introduce PyArg, a Python-based solver and explainer for both abstract argumentation and ASPIC+. A large variety of extension-based semantics allows for flexible evaluation and several explanation functions are available.

Source code Visual interface

Odekerken, D., Bex, F., Borg, A., & Testerink, B. (2022). Approximating Stability for Applied Argument-based Inquiry. Intelligent Systems with Applications.
In argument-based inquiry, agents jointly construct arguments supporting or attacking a topic claim to find out if the claim can be accepted given the agents’ knowledge bases. While such inquiry systems can be used for various forms of automated information intake, several efficiency issues have so far prevented widespread application. In this paper, we aim to tackle these efficiency issues by exploring the notion of stability: can additional information change the justification status of the claim under discussion? Detecting stability is not tractable for every input, since the problem is CoNP-complete, yet in practical applications it is essential to guarantee efficient computation. This makes approximation a viable alternative. We present a sound approximation algorithm that recognises stability for many inputs in polynomial time and discuss several of its properties. In particular, we show that the algorithm is sound and identify constraints on the input under which it is complete. As a final contribution of this paper, we describe how the proposed algorithm is used in three different case studies at the Netherlands Police.

Source code Additional proofs

Odekerken, D., Koops, H. V., & Volk, A. (2021). Improving Audio Chord Estimation by Alignment and Integration of Crowd-Sourced Symbolic Music. Transactions of the International Society for Music Information Retrieval, 4(1), 141-155.
Automatic Chord Estimation (ACE) is a fundamental task in Music Information Retrieval (MIR) and has applications in both music performance and MIR research. The task consists of segmenting a music recording or score and assigning a chord label to each segment. Although it has been a task in the annual benchmarking evaluation MIREX for over 10 years, ACE is not yet a solved problem, since performance has stagnated and modern systems have started to tune themselves to subjective training data. We propose DECIBEL, a new ACE system that exploits heterogeneous musical representations, specifically MIDI and tab files, to improve audio-based ACE methods. From an audio file and a set of MIDI and tab files corresponding to the same popular music song, DECIBEL first estimates chord sequences. For audio, state-of-the-art audio ACE methods are used. MIDI files are aligned to the audio, followed by a MIDI chord estimation step. Tab files are transformed into untimed chord sequences and then aligned to the audio. Next, DECIBEL uses data fusion to integrate all estimated chord sequences into one final output sequence. DECIBEL improves all tested state-of-the-art ACE methods by 0.5 to 13.6 percentage points. This result shows that the integration of crowd-sourced annotations from heterogeneous symbolic music representations using data fusion is a suitable strategy for addressing challenging MIR tasks such as ACE.

Source code Documentation website

Odekerken, D., & Bex, F. J. (2020). Towards Transparent Human-in-the-Loop Classification of Fraudulent Web Shops. In S. Villata, J. Harašta, & P. Křemen (Eds.), Legal Knowledge and Information Systems (Vol. 334, pp. 239-242). (Frontiers in Artificial Intelligence and Applications). IOS Press.
We propose an agent architecture for transparent human-in-the-loop classification. By combining dynamic argumentation with legal case-based reasoning, we create an agent that is able to explain its decisions at various levels of detail and adapts to new situations. It keeps the human analyst in the loop by presenting suggestions for corrections that may change the factors on which the current decision is based and by enabling the analyst to add new factors. We are currently implementing the agent for classification of fraudulent web shops at the Dutch Police.

Odekerken, D., Borg, A., & Bex, F. J. (2020). Estimating Stability for Efficient Argument-based Inquiry. In Computational Models of Argument. Proceedings of COMMA 2020 (Frontiers in Artificial Intelligence and Applications).
We study the dynamic argumentation task of detecting stability: given a specific structured argumentation setting, can adding information change the acceptability status of some propositional formula? Detecting stability is not tractable for every input, but efficient computation is essential in practical applications. We present a sound approximation algorithm that recognises stability for many inputs in polynomial time and we discuss several of its properties. In particular, we show under which constraints on the input our algorithm is complete. The proposed algorithm is currently applied for fraud inquiry at the Dutch National Police - we provide an English demo version that also visualises the output of the algorithm.

Application on the police web site Proofs of the results

Araszkiewicz, M., Amantea, I. A., Chakravarty, S., van Doesburg, R., Dymitruk, M., Garin, M., Gilpin, L., Odekerken, D., & Salehi, S. S. (2020). ICAIL Doctoral Consortium, Montreal 2019. Artificial Intelligence and Law, 28(2), 267-280.
This is a report on the Doctoral Consortium co-located with the 17th International Conference on Artificial Intelligence and Law in Montreal.

Odekerken, D., Testerink, B. J. G., & Bex, F. J. (2019). A method for efficient argument-based inquiry. In Flexible Query Answering Systems: 13th International Conference, FQAS 2019, Amantea, Italy, July 2–5, 2019, Proceedings (Lecture Notes in Artificial Intelligence). Springer Verlag.
In this paper we describe a method for efficient argument-based inquiry. In this method, an agent creates arguments for and against a particular topic by matching argumentation rules with observations gathered by querying the environment. To avoid making superfluous queries, the agent needs to determine if the acceptability status of the topic can change given more information. We define a notion of stability, where a structured argumentation setup is stable if no new arguments can be added, or if adding new arguments will not change the status of the topic. Because determining stability requires hypothesizing over all future argumentation setups, which is computationally very expensive, we define a less complex approximation algorithm and show that this is a sound approximation of stability. Finally, we show how stability (or our approximation of it) can be used in determining an optimal inquiry policy, and discuss how this policy can be used to, for example, determine a strategy in an argument-based inquiry dialogue.

Testerink, B. J. G., Odekerken, D., & Bex, F. J. (2019). AI-assisted message processing for the Netherlands National Police. In Proceedings of the ICAIL 2019 Workshop on AI and the Administrative State (AIAS 2019) CEUR Workshop Proceedings.
The number of messages that the Netherlands National Police (NNP) receives (e.g. from international partner institutes and citizens) grows steadily every year. The NNP has initiated a number of projects to develop artificial intelligence systems that assist in the processing of such messages. In this demo, we show a prototype of one such system that will be used for supporting the processing of messages from international (Interpol) partners.

Schraagen, M. P., Bex, F. J., Odekerken, D., & Testerink, B. J. G. Argumentation-driven information extraction for online crime reports. In International Workshop on Legal Data Analysis and Mining (LeDAM 2018) (CEUR workshop proceedings).
A new system is currently being developed to assist the Dutch National Police in the assessment of crime reports submitted by civilians. This system uses Natural Language Processing techniques to extract observations from text. These observations are used in a formal reasoning system to construct arguments supporting conclusions based on the extracted observations, and possibly ask the complainant who files the report extra questions during the intake process. The aim is to develop a dynamic question-asking system which automatically learns effective and user-friendly strategies. The proposed approach is planned to be integrated in the daily workflow at the Dutch National Police, in order to provide increased efficiency and transparency for processing of crime reports.

This paper presents a corpus-based study on rhythmic patterns in ragtime and jazz. Ragtime and jazz are related genres, but there are open questions on what specifies the two genres. Earlier studies revealed that variations of a particular syncopation pattern, referred to as 121, are among the most frequently used patterns in ragtime music. Literature in musicology states that another pattern, clave, is often heard in jazz, particularly in songs composed before 1945. Using computational tools, this paper tests three hypotheses on the occurrence of 121 syncopation and clave patterns in ragtime and jazz. For this purpose, we introduce a new data set of 252 jazz MIDI files with annotated melody and metadata. We also use the RAG-collection, which consists of around 11000 ragtime MIDI files and metadata. Our analysis shows that syncopation patterns are significantly more frequent in the melody of ragtime pieces than in jazz. Clave on the other hand is found significantly more in jazz melodies than in ragtime. Our findings show that the frequencies of rhythmic patterns differ significantly between music genres, and thus can be used as a feature in automatic genre classification.