Publications

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.

@article{OdekerkenBB24juststabrelevance,
    author =         {Odekerken, Daphne and Bex, Floris and Borg, AnneMarie and Testerink, Bas},
    title =          {Justification, stability and relevance in incomplete argumentation frameworks},
    journal =        {Argument \& Computation},
    year =           {2024},
    pages =          {251--308},
    volume =         {15},
    number =         {3},
    doi =            {10.3233/AAC-230002}
}

This paper reports on an experiment on using case-based reasoning in Dutch administrative law. The use case is decision-support for human medical experts at the Dutch Central Office of Driving Certification who have to decide whether a citizen who applies for a driving licence is fit to drive. Case-based reasoning is investigated for this purposes because of its potential advantages over machine-learning approaches as regards transparency and explainability. Both traditional case-based reasoning, AI & Law models of precedential constraint and their combination are investigated on predictive accuracy relative to a large case base with more than 30.000 cases. A combined model is found to have the highest accuracy. The results indicate that human-in-the-loop support with a tool based on the combined model may be feasible, but whether this is indeed so requires further investigation.

@inproceedings{NouwensBP24cbr,
    author =         {Joep Nouwens and AnneMarie Borg and Henry Prakken},
    title =          {An Application of Case-Based Reasoning to Decision-Making in Dutch Administrative Law},
    booktitle =      {Proceedings of the thirty-seventh Annual Conference on Legal Knowledge and Information Systems (JURIX'24)},
    editor =         {Jarom{\'{\i}}r Savelka and Jakub Harasta and Tereza Novotn{\'{a}} and Jakub M{\'{\i}}sek},
    year =           {2024},
    pages =          {131--141},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {395}
}

In formal argumentation theory, multiple argumentation-based explanation methods have been formulated based on ideas from social and cognitive science. However, these have not yet been empirically validated. One such idea is that information in an explanation needs to be related; in argumentation-based explanations, this has been captured as there being an attack path between arguments. This study describes and empirically validates two types of relatedness, related admissibility and directly related admissibility. This was done by instructing participants to select arguments from an argumentation framework to explain another argument in this framework. These explanations selected by the participants were compared to argumentation-based explanations that use relatedness. We found that both forms of relatedness are cognitively plausible. This gives insight into how argumentation theory can be applied in the real world to provide explanations.

@inproceedings{ScheffersBB24relatedexplanations,
    author =         {Roos Scheffers and Floris Bex and AnneMarie Borg},
    title =          {Related Explanations in Formal Argumentation, an Empirical Study},
    booktitle =      {Proceedings of the tenth International Conference on Computational Models of Argument (COMMA'24)},
    editor =         {Chris Reed and Matthias Thimm and Tjitze Rienstra},
    year =           {2024},
    pages =          {264--276},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {388}
}

In this paper, we present an agent-based model for studying the impact of ‘myside bias’ on the argumentative dynamics in scientific communities. Recent insights in cognitive science suggest that scientific reasoning is influenced by ‘myside bias’. This bias manifests as a tendency to prioritize the search and generation of arguments that support one’s views rather than arguments that undermine them. Additionally, individuals tend to apply more critical scrutiny to opposing stances than to their own. Although myside bias may pull individual scientists away from the truth, its effects on communities of reasoners remain unclear. The aim of our model is two-fold: first, to study the argumentative dynamics generated by myside bias, and second, to explore which mechanisms may act as a mitigating factor against its pernicious effects. Our results indicate that biased communities are epistemically less successful than non-biased ones, and that they also tend to be less polarized than non-biased ones. Moreover, we find that two socio-epistemic mechanisms help communities to mitigate the effect of the bias: the presence of a common filter on weak arguments, which can be interpreted as shared beliefs, and an equal distribution of agents for each alternative at the start of the scientific debate.

@article{DupuisdeTarleMBPRSS24MySideBias,
    author =         {Louise Dupuis de Tarl{\'{e}} and Matteo Michelini and AnneMarie Borg and Gabriella Pigozzi and Juliette Rouchier and Dunja Seselja and Christian Stra{\ss}er},
    title =          {An Agent-Based Model of MySide Bias in Scientific Debates},
    journal =        {Journal of Artificial Societies and Social Simulation},
    year =           {2024},
    volume =         {27},
    number =         {3}
}

We discuss explanations for formal (abstract and structured) argumentation – the question whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how selection based on necessity and sufficiency can be captured in our basic framework and discuss a real-life application.

@article{BorgB24MinNecSuff,
    author =         {AnneMarie Borg and Floris Bex},
    title =          {Minimality, necessity and sufficiency for argumentation and explanation},
    journal =        {International Journal of Approximate Reasoning},
    volume =         {168},
    pages =          {109143},
    year =           {2024},
    doi =            {10.1016/J.IJAR.2024.109143}
}

Logical argumentation is a well-known approach to modeling non-monotonic reasoning with conflicting information. In this paper we provide a comprehensive postulate-based study of properties of logical argumentation frameworks and a full characterization of their semantics and inference relations. In this way we identify well-behaved formal argumentative models of drawing logically justified inferences from a given set of possibly conflicting defeasible, as well as strict assumptions. Given some desiderata in terms of rationality postulates, we consider the conditions that an argumentation framework should fulfill for the desiderata to hold. One purpose of this approach is to assist designers to “plug-in” pre-defined formalisms according to actual needs. To this end, we present a classification of argumentation frameworks relative to the types of attacks they implement. In turn, for each class we determine which desiderata are satisfied. Our study is highly abstract, supposing only a minimal set of requirements on the considered underlying deductive systems, and in this way covering a broad range of formalisms, including classical, intuitionistic and modal logics.

@article{ArieliBS23PostulateDriven,
    author =         {Arieli, Ofer and Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {A Postulate-Driven Study of Logical Argumentation},
    journal =        {Artificial Intelligence},
    year =           {2023},
    pages =          {103966},
    doi =            {10.1016/J.ARTINT.2023.103966}
}

We demonstrate a human-centred explanation method for rule-based automated decision-making systems in the legal domain. This explanation method uses a graph database to enable question-driven explanations and multimedia display. This way, we can tailor the explanation to the user. We show the implementation of our explanation method in a real-world scenario at the Dutch Tax and Customs Administration.

@inproceedings{ZuurmondBKWhuman-centred,
    author =         {Suzan Zuurmond and AnneMarie Borg and Matthijs van Kempen and Remi Wieten},
    title =          {Human-Centred Explanation of Rule-Based Decision-Making Systems in the Legal Domain: Demonstration},
    booktitle =      {Proceedings of the thirty-sixth Annual Conference on Legal Knowledge and Information Systems (JURIX'23)},
    editor =         {Giovanni Sileno and Jerry Spanakis and Gijs van Dijck},
    year =           {2023},
    pages =          {375--378},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {379}
}

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.

@inproceedings{OdekerkenBBPyArg2,
    author =         {Daphne Odekerken and AnneMarie Borg and Matti Berthold},
    title =          {Demonstrating PyArg 2.0},
    booktitle =      {Proceedings of the 7th Workshop on Advances in Argumentation in Artificial
                  Intelligence (AI{\^{}}3'23)},
    editor =         {Gianvincenzo Alfano and Stefano Ferilli},
    year =           {2023},
    pages =          {14},
    publisher =      {CEUR-WS.org},
    series =         {{CEUR} Workshop Proceedings},
    volume =         {3546}
}

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+.

@inproceedings{OdekerkenLBWJincomplete,
    author =         {Daphne Odekerken and Tuomo Lehtonen and AnneMarie Borg and Johannes Peter Wallner and Matti J{\"{a}}rvisalo},
    title =          {Argumentative Reasoning in {ASPIC+} under Incomplete Information},
    booktitle =      {Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning (KR'23)},
    editor =         {Pierre Marquis and Tran Cao Son and Gabriele Kern{-}Isberner},
    year =           {2023},
    pages =          {531--541},
    publisher =      {IJCAI Organization}
}

We show that logic-based argumentation, and in particular sequent-based frameworks, is a robust argumentative setting for abductive reasoning and explainable artificial intelligence.

@inproceedings{ArieliBHS22abductivereasoning,
    author =         {Ofer Arieli and AnneMarie Borg and Matthis Hesse and Christian Stra{\ss}er},
    title =          {Abductive Reasoning with Sequent-Based Argumentation},
    booktitle =      {Proceedings of the twentieth International Workshop on Non-Monotonic Reasoning (NMR'22)},
    editor =         {Ofer Arieli and Giovanni Casini and Laura Giordano},
    year =           {2022},
    pages =          {143--146},
    publisher =      {CEUR-WS.org},
    series =         {{CEUR} Workshop Proceedings},
    volume =         {3197}
}

Explainable artificial intelligence (XAI) has gained increasing interest in recent years in the argumentation community. In this paper we consider this topic in the context of logic-based argumentation, showing that the latter is a particularly promising paradigm for facilitating explainable AI. In particular, we provide two representations of abductive reasoning by sequent-based argumentation frameworks and show that such frameworks successfully cope with related challenges, such as the handling of synonyms, justifications, and logical equivalences.

@inproceedings{ArieliBHS22logicbased,
    author =         {Ofer Arieli and AnneMarie Borg and Matthis Hesse and Christian Stra{\ss}er},
    title =          {Explainable Logic-Based Argumentation},
    booktitle =      {Proceedings of the ninth International Conference on Computational Models of Argument (COMMA'22)},
    editor =         {Francesca Toni and Sylwia Polberg and Richard Booth and Martin Caminada and Hiroyuki Kido},
    year =           {2022},
    pages =          {32--43},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {353}
}

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.

@inproceedings{OdekerkenBB22Relevance,
    author =         {Daphne Odekerken and AnneMarie Borg and Floris Bex},
    title =          {Stability and Relevance in Incomplete Argumentation Frameworks},
    booktitle =      {Proceedings of the ninth International Conference on Computational Models of Argument (COMMA'22)},
    editor =         {Francesca Toni and Sylwia Polberg and Richard Booth and Martin Caminada and Hiroyuki Kido},
    year =           {2022},
    pages =          {272--283},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {353}
}

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.

@inproceedings{BorgO22PyArg,
    author =         {AnneMarie Borg and Daphne Odekerken},
    title =          {PyArg for Solving and Explaining Argumentation in Python: Demonstration},
    booktitle =      {Proceedings of the ninth International Conference on Computational Models of Argument (COMMA'22)},
    editor =         {Francesca Toni and Sylwia Polberg and Richard Booth and Martin Caminada and Hiroyuki Kido},
    year =           {2022},
    pages =          {349--350},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {353}
}

Modeling contrastive explanations for the use in artificial intelligence (AI) applications is an important research branch within the field of explainable AI (XAI). However, most of the existing contrastive XAI approaches are not based on the findings in the literature from the social sciences on contrastiveness in human reasoning and human explanations. In this work we collect the various types of contrastiveness proposed in the literature and model these with formal argumentation. The result is a variety of argumentation-based methods for contrastive explanations, based on the available literature and applicable in a wide variety of AI-applications.

@inproceedings{BorgB22Modeling,
    author =         {Borg, AnneMarie and Bex, Floris},
    title =          {Modeling Contrastiveness in Argumentation},
    booktitle =      {Proceedings of the 22nd Workshop on Computational Models of Natural Argument (CMNA'22)},
    publisher =      {CEUR-WS},
    year =           {2022},
    pages =          {1--12},
    editor =         {Floriana Grasso and Nancy Green and Jodi Schneider and Simon Wells}
}

In this paper we study everyday explanations for classification tasks with formal argumentation. Everyday explanations describe how humans explain in day-to-day life, which is important when explaining decisions of AI systems to lay users. We introduce EVAX, a model-agnostic explanation method for classifiers with which contrastive, selected and social explanations can be generated. The resulting explanations can be adjusted in their size and retain high fidelity scores (an average of 0.95)

@inproceedings{vanLenteBB22EVAX,
    author =         {van Lente, Jowan and Borg, AnneMarie and Bex, Floris},
    title =          {Everyday Argumentative Explanations for Classification},
    booktitle =      {Proceedings of the 1st Workshop on Argumentation \& Machine Learning (ArgML'22},
    publisher =      {CEUR-WS},
    year =           {2022},
    pages =          {14--26},
    editor =         {Isabelle Kuhlmann and Jack Mumford and Stefan Sarkadi}
}

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.

@article{OdekerkenBBT22approx,
    author =         {Odekerken, Daphne and Bex, Floris and Borg, AnneMarie and Testerink, Bas},
    title =          {Approximating Stability for Applied Argument-based Inquiry.},
    journal =        {Intelligent Systems with Applications},
    year =           {2022},
    pages =          {200110}
}

In this paper we discuss contrastive explanations for formal argumentation — the question why one argument (the fact) can be accepted, whilst another argument (the foil) cannot be accepted. We show under which conditions contrastive explanations in abstract argumentation are meaningful, and how argumentation allows us to make implicit foils explicit.

@inproceedings{BorgB22Contrastive,
    author =         {Borg, AnneMarie and Bex, Floris},
    title =          {Contrastive Explanations for Argumentation-Based Conclusions},
    booktitle =      {Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems (AAMAS'22)},
    publisher =      {International Foundation for Autonomous Agents and Multiagent Systems {(IFAAMAS)}},
    year =           {2022},
    pages =          {1551--1553},
    editor =         {Piotr Faliszewski and Viviana Mascardi and Catherine Pelachaud and Matthew E. Taylor}
}

As AI systems are increasingly applied in real-life situations, it is essential that such systems can give explanations that provide insight into the underlying decision models and techniques. Thus, users can understand, trust and validate the system, and experts can verify that the system works as intended. At the Dutch National Police several applications based on computational argumentation are in use, with police analysts and Dutch citizens as possible users. In this paper we show how a basic framework of explanations aimed at explaining argumentation-based conclusions can be applied to these applications at the police.

@inproceedings{BorgB21Police,
    author =         {Borg, AnneMarie and Bex, Floris},
    title =          {Explaining Arguments at the Dutch National Police},
    booktitle =      {AI Approaches to the Complexity of Legal Systems},
    series =         {Lecture Notes in Computer Science},
    volume =         {13048},
    editor =         {Palmirani, M. and Rodr{\' i}guez-Doncel, V. and Casanovas, P. and Pagallo, U. and Sartor, G.},
    year =           {2021},
    pages =          {183--197},
    doi =            {10.1007/978-3-030-89811-3_13}
}

Enforcement, adjusting an argumentation framework such that a certain set of arguments becomes acceptable, is an important research topic within the study of dynamic argumentation, but one that has been little studied for structured argumentation. In this paper we study enforcement in a general structured argumentation setting. In particular, we study conditions on the argumentation setting and the knowledge base that ensure (or prevent) the acceptability of sets of formulas for structured argumentation frameworks.

@inproceedings{BorgB21Enforcement,
    author =         {Borg, AnneMarie and Bex, Floris},
    title =          {Enforcing Sets of Formulas in Structured Argumentation},
    booktitle =      {Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR'21)},
    editor =         {Bienvenu, M. and Lakemeyer, G. and Erdem, E.},
    year =           {2021},
    pages =          {130--140},
    doi =            {10.24963/kr.2021/13}
}

In this paper we provide a detailed analysis of the inference process induced by logical argumentation frameworks. The frameworks may be defined with respect to any propositional language and logic, different arguments that represent deductions in the logic, various support-based attack relations between arguments, and all the complete Dung-style semantics for the frameworks. We show that, ultimately, for characterizing the inference process with respect to a given framework, extension-based semantics may be divided into two types: single-extension and multiple-extension, which induce respective kinds of entailment relations. These entailments are further classified by the way they tolerate new information (nonmonotonicity-related properties) and maintain conflicts among arguments (inconsistency-related properties).

@inproceedings{ArieliBS21Character,
    author =         {Arieli, Ofer and Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {Characterizations and Classifications of Argumentative Entailments},
    booktitle =      {Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR'21)},
    editor =         {Bienvenu, M. and Lakemeyer, G. and Erdem, E.},
    year =           {2021},
    pages =          {52--62},
    doi =            {10.24963/kr.2021/6}
}

In this paper, we discuss necessary and sufficient explanations – the question whether and why a certain argument or claim can be accepted (or not) – for abstract and structured argumentation. Given a framework with which explanations for argumentation-based conclusions can be derived, we study necessity and sufficiency: what (sets of) arguments are necessary or sufficient for the (non-)acceptance of an argument or claim? We will show that necessary and sufficient explanations can be strictly smaller than minimal explanations, while still providing all the reasons for a conclusion and we discuss their usefulness in a real-life application.

@inproceedings{BorgB21NecSuff,
    author =         {AnneMarie Borg and Floris Bex},
    title =          {Necessary and Sufficient Explanations for Argumentation-Based Conclusions},
    booktitle =      {Proceedings of the 16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU'21)},
    editor =         {Vejnarov{\'a}, Ji{\v{r}}ina and Wilson, Nic},
    year =           {2021},
    publisher =      {Springer},
    series =         {Lecture Notes in Artificial Intelligence},
    volume =         {12897},
    pages =          {45--58},
    doi =            {10.1007/978-3-030-86772-0_4}
}

As AI systems are increasingly applied in real-life situations, it is essential that such systems can give explanations that provide insight into the underlying decision models and techniques. Thus, users can understand, trust and validate the system, and experts can verify that the system works as intended. At the Dutch National Police several applications based on computational argumentation are in use, with police analysts and Dutch citizens as possible users. In this paper we show how a basic framework of explanations aimed at explaining argumentation-based conclusions can be applied to these applications at the police

@inproceedings{BorgB20PoliceIntuitive,
    author =         {AnneMarie Borg and Floris Bex},
    title =          {Explaining Arguments at the Dutch National Police},
    booktitle =      {Proceedings of the 3rd Explainable & Responsible AI in Law Workshop (XAILA'20)},
    year =           {2020},
    publisher =      {CEUR-WS},
    url =            {http://ceur-ws.org/Vol-2891/#XAILA-2020_paper_5}
}

We study the logical foundations of Dung-style argumentation frameworks. Logic-based methods in the context of argumentation theory are described from two perspectives: (a) a survey of logic-based instantiations of argumentation frameworks, their properties and relations, and (b) a review of logical methods for the study of argumentation dynamics. In this chapter we restrict ourselves to Tarskian logics, based on (propositional) languages and corresponding (constructive) semantics or syntactic rule-based systems.

@article{ArieliBHS21LogicBased,
    author =         {Arieli, Ofer and Borg, AnneMarie and Heyninck, Jesse and Stra{\ss}er, Christian},
    title =          {Logic-based approaches to formal argumentation},
    journal =        {Journal of Applied Logics – IfCoLog Journal of Logics and their Applications},
    year =           {2021},
    volume =         {8},
    number =         {6},
    pages =          {1793--1898}
}

We discuss explanations for formal (abstract and structured) argumentation – the question of whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how different types of explanations can be captured in our basic framework, discuss a real-life application and formally compare our framework to existing work.

@article{BorgB21Basic,
    author =         {AnneMarie Borg and Floris Bex},
    title =          {A Basic Framework for Explanations in Argumentation},
    journal =        {IEEE Intelligent Systems},
    volume =         {36},
    number =         {2},
    pages =          {25--35},
    year =           {2021},
    doi =            {10.1109/MIS.2021.3053102}
}

In this paper we introduce hypersequent-based frameworks for the modelling of defeasible reasoning by means of logic-based argumentation and the induced entailment relations. These structures are an extension of sequent-based argumentation frameworks, in which arguments and the attack relations among them are expressed not only by Gentzen-style sequents, but by more general expressions, called hypersequents. This generalization allows us to overcome some of the known weaknesses of logical argumentation frameworks and to prove several desirable properties of the entailments that are induced by the extended (hypersequent-based) frameworks. It also allows us to incorporate as the deductive base of our formalism some well-known logics (like the intermediate logic LC, the modal logic S5, and the relevance logic RM), which lack cut-free sequent calculi, and so are not adequate for standard sequent-based argumentation. We show that hypersequent-based argumentation yields robust defeasible variants of these logics, with many desirable properties.

@article{BorgSA21Hypersequents,
    author =         {Borg, AnneMarie and Stra{\ss}er, Christian and Arieli, Ofer},
    title =          {A Generalized Proof-Theoretic Approach to Logical Argumentation Based on Hypersequents},
    journal =        {Studia Logica},
    year =           {2021},
    volume =         {109},
    pages =          {167--238},
    doi =            {10.1007/s11225-020-09906-1}
}

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.

@inproceedings{OdekerkenBB20EfficientStability,
    author =         {Daphne Odekerken and AnneMarie Borg and Floris Bex},
    title =          {Estimating Stability for Efficient Argument-Based Inquiry},
    booktitle =      {Proceedings of the eighth International Conference on Computational Models of Argument (COMMA'20)},
    editor =         {Henry Prakken and Stefano Bistarelli and Francesco Santini and Carlo Taticchi},
    year =           {2020},
    pages =          {307--318},
    publisher =      {{IOS} Press},
    series =         {Frontiers in Artificial Intelligence and Applications},
    volume =         {326}
}

In many expert and everyday reasoning contexts it is very useful to reason on the basis of defeasible assumptions. For instance, if the information at hand is incomplete we often use plausible assumptions, or if the information is conflicting we interpret it as consistently as possible. In this paper sequent-based argumentation, a form of logical argumentation in which arguments are represented by a sequent, is extended to incorporate defeasible assumptions. The resulting assumptive framework is general, in that several other approaches to reasoning with assumptions from the literature can adequately be represented in it. Moreover, assumptive sequent-based argumentation has many desirable properties. It will be shown that assumptive sequent-based argumentation can easily be extended to a prioritized setting, it satisfies rationality postulates and reasoning with maximally consistent subsets can be represented in it.

@article{Borg20Assumptions,
    author =         {Borg, AnneMarie},
    title =          {Assumptive Sequent-Based Argumentation},
    journal =        {Journal of Applied Logics – IfCoLog Journal of Logics and their Applications},
    year =           {2020},
    volume =         {7},
    number =         {3},
    pages =          {227--294}
}

Logical argumentation is a well-known approach to modelling nonmonotonic reasoning with conflicting information. In this paper we provide a proof-theoretic study of properties of logical argumentation frameworks. Given some desiderata in terms of rationality postulates, we consider the conditions that an argumentation framework should fulfill for the desiderata to hold. The rationality behind this approach is to assist designers to “plug-in” pre-defined formalisms according to actual needs. This work extends related research on the subject in several ways: more postulates are characterized, a more abstract notion of arguments is considered, and it is shown how the nature of the attack rules (subset attacks versus direct attacks) affects the properties of the whole setting.

@inproceedings{ArieliBS20ProofTheoretic,
    author =         {Arieli, Ofer and Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {A Proof Theoretic Perspective of Logical Argumentation},
    booktitle =      {Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS'20)},
    year =           {2020},
    pages =          {557--562}
}

Formal models of scientific inquiry, aimed at capturing socio-epistemic aspects underlying the process of scientific research, have become an important method in formal social epistemology and philosophy of science. In this introduction to the special issue we provide a historical overview of the development of formal models of this kind and analyze their methodological contributions to discussions in philosophy of science. In particular, we show that their significance consists in different forms of ‘methodological iteration’ (Elliott 2012) whereby the models initiate new lines of inquiry, isolate and clarify problems with existing knowledge claims, and stimulate further research.

@article{SeseljaSB20FormalModels,
    author =         {{\v{S}}e{\v{s}}elja, Dunja and Stra{\ss}er, Christian and Borg, AnneMarie},
    title =          {Formal Models of Scientific Inquiry in a Social Context: An Introduction},
    journal =        {Journal for General Philosophy of Science volume},
    year =           {2020},
    volume =         {51},
    pages =          {211--217},
    doi =            {10.1007/s10838-020-09502-w}
}

This is a survey of some recent results relating Dung-style semantics for different types of logical argumentation frameworks and several forms of reasoning with maximally consistent sets (MCS) of premises. The related formalsims are also examined with respect to some rationality postulates and are carried on to corresponding proof systems for non-monotonic reasoning.

@article{ArieliBH19SurveyMCS,
    author =         {Arieli, Ofer and Borg, AnneMarie and Heyninck, Jesse},
    title =          {A review of the relations between logical argumentation and reasoning with maximal consistency},
    journal =        {Annals of Mathematics and Artificial Intelligence},
    year =           {2019},
    volume =         {87},
    pages =          {187--226},
    doi =            {10.1007/s10472-019-09629-7}
}

Recent studies of scientific interaction based on agent-based models (ABMs) suggest that a crucial factor conducive to efficient inquiry is what Zollman (2010) has dubbed ‘transient diversity’. It signifies a process in which a community engages in parallel exploration of rivaling theories lasting sufficiently long for the community to identify the best theory and to converge on it. But what exactly generates transient diversity? And is transient diversity a decisive factor when it comes to the efficiency of inquiry? In this paper we examine the impact of different conditions on the efficiency of inquiry, as well as the relation between diversity and efficiency. This includes certain diversity-generating mechanisms previously proposed in the literature (such as different social networks and cautious decision-making), as well as some factors that have so far been neglected (such as evaluations underlying theory-choice performed by scientists). This study is obtained via an argumentation-based ABM (Borg et al. 2017, 2018). Our results suggest that cautious decision-making does not always have a significant impact on the efficiency of inquiry while different evaluations underlying theory-choice and different social networks do. Moreover, we find a correlation between diversity and a successful performance of agents only under specific conditions, which indicates that transient diversity is sometimes not the primary factor responsible for efficiency. Altogether, when comparing our results to those obtained by structurally different ABMs based on Zollman’s work, the impact of specific factors on efficiency of inquiry, as well as the role of transient diversity in achieving efficiency, appear to be highly dependent on the underlying model.

@article{BorgFSS19TransientDiversity,
    author =         {Borg, AnneMarie and Frey, Daniel and {\v{S}}e{\v{s}}elja, Dunja and Stra{\ss}er, Christian},
    title =          {Theory-Choice, Transient Diversity and the Efficiency of Scientific Inquiry},
    journal =        {European Journal for Philosophy of Science},
    year =           {2019},
    volume =         {9},
    number =         {2},
    doi =            {10.1007/s13194-019-0249-5}
}

Reasoning with the maximally consistent subsets (MCS) of the premises is a well-known approach for handling contradictory information. In this paper we consider several variations of this kind of reasoning, for each one we introduce two complementary computational methods that are based on logical argumentation theory. The difference between the two approaches is in their ways of making consequences: one approach is of a declarative nature and is related to Dung-style semantics for abstract argumentation, while the other approach has a more proof-theoretical flavor, extending Gentzen-style sequent calculi. The outcome of this work is a new perspective on reasoning with MCS, which shows a strong link between the latter and argumentation systems, and which can be generalized to some related formalisms. As a by-product of this we obtain soundness and completeness results for the dynamic proof systems with respect to several of Dung’s semantics. In a broader context, we believe that this work helps to better understand and evaluate the role of logic-based instantiations of argumentation frameworks.

@article{ArieliBS18MCS,
    author =         {Arieli, Ofer and Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {Reasoning with maximal consistency by argumentative approaches},
    journal =        {Journal of Logic and Computation},
    year =           {2018},
    volume =         {28},
    number =         {7},
    pages =          {1523--1563},
    doi =            {10.1093/log-com/exy027}
}

In many expert and everyday reasoning contexts it is very useful to reason on the basis of defeasible assumptions. For instance, if the information at hand is incomplete we often use plausible assumptions, or if the information is conflicting we interpret it as consistent as possible. In this paper sequent-based argumentation, a form of logical argumentation in which arguments are represented by a sequent, is extended to incorporate assumptions. The resulting assumptive framework is general, in that some other approaches to reasoning with assumptions can adequately be represented in it. To exemplify this, we show that assumption-based argumentation can be expressed in assumptive sequent-based argumentation.

@inproceedings{Borg18Assumptions,
    author =         {Borg, AnneMarie},
    title =          {Equipping Sequent-Based Argumentation with Defeasible Assumptions},
    journal =        {Computational Models of Argument (COMMA'18)},
    editor =         {Sanjay Modgil and Katarzyna Budzynska and John Lawrence},
    year =           {2018},
    serie =          {Frontiers in Artificial Intelligence and Applications},
    volume =         {305},
    pages =          {129--136},
    doi =            {10.3233/978-1-61499-906-5-129}
}

We study properties related to relevance in non-monotonic consequence relations obtained by systems of structured argumentation. Relevance desiderata concern the robustness of a consequence relation under the addition of irrelevant information. For an account of what (ir)relevance amounts to we use syntactic and semantic considerations. Syntactic criteria have been proposed in the domain of relevance logic and were recently used in argumentation theory under the names of non-interference and crash-resistance. The basic idea is that the conclusions of a given argumentative theory should be robust under adding information that shares no propositional variables with the original database. Some semantic relevance criteria are known from non-monotonic logic. For instance, cautious monotony states that if we obtain certain conclusions from an argumentation theory, we may expect to still obtain the same conclusions if we add some of them to the given database. In this paper we investigate properties of structured argumentation systems that warrant relevance desiderata.

@inproceedings{BorgS18Relevance,
    author =         {Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {Relevance in Structured Argumentation},
    booktitle =      {Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18)},
    editor =         {Lang, J.},
    year =           {2018},
    pages =          {1753--1759},
    doi =            {10.24963/ijcai.2018/242}
}

In this paper we introduce hypersequent-based frameworks for the modeling of defeasible reasoning by means of logic-based argumentation. These frameworks are an extension of sequent-based argumentation frameworks, in which arguments are represented not only by sequents, but by more general expressions, called hypersequents. This generalization allows to incorporate, as the deductive-base of our formalism, some well-studied logics like the modal logic S5, the relevant logic RM, and Gödel–Dummett logic LC, to which no cut-free sequent calculi are known. In this paper we take S5 as the core logic and show that the hypersequent-based argumentation frameworks that are obtained in this case yield a robust defeasible variant of S5 with several desirable properties.

@inproceedings{BorgA18S5,
    author =         {Borg, AnneMarie and Arieli, Ofer},
    title =          {Hypersequential Argumentation Frameworks: An Instantiation in the Modal Logic S5},
    booktitle =      {Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS'18)},
    year =           {2018},
    pages =          {1097--1104}
}

In this paper we integrate priorities in sequent-based argumentation. The former is a useful and extensively investigated tool in the context of non-monotonic reasoning, and the latter is a modular and general way of handling logical argumentation. Their combination offers a platform for representing and reasoning with maximally consistent subsets of prioritized knowledge bases. Moreover, many frameworks of the resulting formalisms satisfy common rationality postulates and other desirable properties, like conflict preservation.

@inproceedings{ArieliBS18Priorities,
    author =         {Arieli, Ofer and Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {Prioritized Sequent-Based Argumentation},
    booktitle =      {Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS'18)},
    year =           {2018},
    pages =          {1105--1113}
}

The question whether increased interaction among scientists is beneficial or harmful for their efficiency in acquiring knowledge has in recent years been tackled by means of agent-based models (ABMs) (e.g. Zollman 2007, 2010; Grim 2009; Grim et al. 2013). Nevertheless, the relevance of some of these results for actual scientific practice has been questioned in view of specific parameter choices used in the simulations (Rosenstock et al. 2016). In this paper we present a novel ABM that aims at tackling the same question, while representing scientific interaction in terms of argumentative exchange. In this way we examine the robustness of previously obtained results under different modeling choices.

@article{BorgFSS18ScientificInteraction,
    author =         {Borg, AnneMarie and Frey, Daniel and {\v{S}}e{\v{s}}elja, Dunja and Stra{\ss}er, Christian},
    title =          {Epistemic effects of scientific interaction: approaching the question with an argumentative agent-based model},
    journal =        {Historical Social Research},
    year =           {2018},
    volume =         {43},
    number =         {1},
    pages =          {285--307},
    doi =            {10.12759/hsr.43.2018.1.285-307}
}

In this paper we introduce hypersequent-based frameworks for the modeling of defeasible reasoning by means of logic-based argumentation. These frameworks are an extension of sequent-based argumentation frameworks, in which arguments are represented not only by sequents, but by more general expressions, called hypersequents. This generalization allows us to overcome some of the weaknesses of logical argumentation reported in the literature and to prove several desirable properties, stated in terms of rationality postulates. For this, we take the relevance logic RM as the deductive base of our formalism. This logic is regarded as “by far the best understood of the Anderson-Belnap style systems” (Dunn and Restall, Handbook of Philosophical Logic, vol. 6). It has a clear semantics in terms of Sugihara matrices, as well as sound and complete Hilbert- and Gentzen-type proof systems. The latter are defined by hypersequents and admit cut elimination. We show that hypersequent-based argumentation yields a robust defeasible variant of RM with many desirable properties (e.g., rationality postulates and crash-resistance).

@inproceedings{BorgAS17RM,
    author =         {Borg, AnneMarie and Arieli, Ofer and Stra{\ss}er, Christian},
    title =          {Hypersequent-Based Argumentation: An Instantiation in the Relevance Logic RM},
    booktitle =      {Proceedings of the 4th International Workshop on Theory and Applications of Formal Argumentation (TAFA'17)},
    editor =         {Black, Elizabeth and Modgil, Sanjay and Oren, Nir},
    year =           {2018},
    publisher =      {Springer},
    series =         {Lecture Notes in Computer Science},
    volume =         {10757},
    pages =          {17--34},
    doi =            {10.1007/978-3-319-75553-3_2}
}

In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. The model is an improved variant of the ABM introduced in [3], which is based on abstract argumentation frameworks. The current model employs a more refined notion of social networks and a more realistic representation of knowledge acquisition than the previous variant. Moreover, it includes two criteria of success: a monist and a pluralist one, reflecting different desiderata of scientific inquiry. Our findings suggest that, given a reasonable ratio between research time and time spent on communication, increasing the degree of connectedness of the social network tends to improve the efficiency of scientists.

@inproceedings{BorgFSS17NetworkEffects,
    author =         {Borg, AnneMarie and Frey, Daniel and {\v{S}}e{\v{s}}elja, Dunja and Stra{\ss}er, Christian},
    title =          {Examining Network Effects in an
Argumentative Agent-Based Model of Scientific Inquiry},
    booktitle =      {Proceedings of the 6th International Workshop on Logic, Rationality, and Interaction (LORI'17)},
    editor =         {Baltag, Alexandru and Seligman, Jeremy and Yamada, Tomoyuki},
    year =           {2017},
    publisher =      {Springer},
    series =         {Lecture Notes in Computer Science},
    volume =         {10455},
    pages =          {391--406},
    doi =            {10.1007/978-3-662-55665-8_27}
}

It has been shown that entailments based on the maximally consistent subsets (MCS) of a given set of premises can be captured by Dung-style semantics for argumentation frameworks. This paper shows that these links are much tighter and go way beyond simplified forms of reasoning with MCS. Among others, we consider different types of entailments that these kinds of reasoning induce, extend the framework for arbitrary (not necessarily maximal) consistent subsets, and incorporate non-classical logics. The introduction of declarative methods for reasoning with MCS by means of (sequent-based) argumentation frameworks provides, in particular, a better understanding of logic-based argumentation and allows to reevaluate some negative results concerning the latter.

@inproceedings{ArieliBS17ArgMCS,
    author =         {Arieli, Ofer and Borg, AnneMarie and Stra{\ss}er, Christian},
    title =          {Argumentative Approaches to Reasoning with Consistent Subsets of Premises},
    booktitle =      {Proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE'17)},
    editor =         {Benferhat, Salem and Tabia, Karim and Ali, Moonis},
    year =           {2017},
    publisher =      {Springer},
    series =         {Lecture Notes in Computer Science},
    volume =         {10350},
    pages =          {455--465},
    doi =            {10.1007/978-3-319-60042-0_56}
}

In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. As such, the ABM is a computational tool for tackling issues in the domain of scientific methodology and science policy. In contrast to existing ABMs of science, our model aims to represent the argumentative dynamics that underlies scientific practice. To this end we employ abstract argumentation theory as the core design feature of the model

@inproceedings{BorgFSS17ArgABM,
    author =         {Borg, AnneMarie and Frey, Daniel and {\v{S}}e{\v{s}}elja, Dunja and Stra{\ss}er, Christian},
    title =          {An Argumentative Agent-Based Model of Scientific Inquiry},
    booktitle =      {Proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE'17)},
    editor =         {Benferhat, Salem and Tabia, Karim and Ali, Moonis},
    year =           {2017},
    publisher =      {Springer},
    series =         {Lecture Notes in Computer Science},
    volume =         {10350},
    pages =          {507--510},
    doi =            {10.1007/978-3-319-60042-0_56}
}

Justification logics were introduced by Artemov in 1995 to provide intuitionistic logic with a classical provability semantics, a problem originally posed by Gödel. Justification logics are refinements of modal logics and formally connected to them by so-called realization theorems. A constructive proof of a realization theorem typically relies on a cut-free sequent-style proof system for the corresponding modal logic. A uniform realization theorem for all the modal logics of the so-called modal cube, i.e., for the extensions of the basic modal logic K with any subset of the axioms d, t, b, 4, and 5, has been proven using nested sequents. However, the proof was not modular in that some realization theorems required postprocessing in the form of translation on the justification logic side. This translation relied on additional restrictions on the language of the justification logic in question, thus, narrowing the scope of realization theorems. We present a fully modular proof of the realization theorems for the modal cube that is based on modular nested sequents introduced by Marin and Straßburger.

@inproceedings{BorgK15Realization,
    author =         {AnneMarie Borg and Roman Kuznets},
    title =          {Realization Theorems for Justification Logics: Full Modularity},
    booktitle =      {Proceedings of the 24th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods},
    editor =         {De Nivelle, Hans},
    year =           {2015},
    publisher =      {Springer},
    series =         {Lecture Notes in Computer Science},
    volume =         {9323},
    pages =          {221--236},
    doi =            {10.1007/978-3-319-24312-2_16}
}