Author name cluster

Martin Diller

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

NMR Workshop 2025 Conference Paper

ABA Disputes in ASP: Advancing Argument Games through Multi-Shot Solving

Martin Diller
Piotr Gorczyca

Argumentation games, which model reasoning as adversarial dialogue, offer intuitive and explainable mechanisms for decision-making in AI. However, their implementation has lagged behind inference-focused approaches, particularly in structured argumentation frameworks like assumption-based argumentation (ABA). This work presents, to our knowledge, the first application of multi-shot answer set programming (ASP) for implementing argument games, focusing on ABA dispute derivations. Leveraging a recent rule-based representation of ABA disputes, our method combines a declarative program with lightweight script-based control of multi-shot aspects, yielding a modular and adaptable system. We extend this core approach to support alternative games and show how it can also be used to implement argument games for Dung’s abstract argumentation formalism. Empirical results show that our implementation outperforms existing ABA dispute systems. We also introduce an approximate variant that further improves efficiency – reaching the level of the best current inference-focused ABA system – while maintaining perfect specificity (i. e. true negative rate), demonstrating the practical value of multi-shot ASP, particularly in interactive settings where explainability is key.

Details

KR Conference 2025 Conference Paper

Grounding Rule-Based Argumentation Using Datalog

Martin Diller
Sarah Alice Gaggl
Philipp Hanisch
Giuseppina Monterosso
Fritz Rauschenbach

ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.

PDF Details DOI

JAIR Journal 2022 Journal Article

Admissibility in Probabilistic Argumentation

Nikolai Käfer
Christel Baier
Martin Diller
Clemens Dubslaff
Sarah Alice Gaggl
Holger Hermanns

Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion for classical reasoning in abstract argumentation frameworks, it has barely been reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of abstract argumentation based on probabilistic notions of admissibility. Our approach follows the natural idea of defining probabilistic semantics for abstract argumentation by systematically imposing constraints on the joint probability distribution on the sets of arguments, rather than on probabilities of single arguments. As a result, there might be either a uniquely defined distribution satisfying the constraints, but also none, many, or even an infinite number of satisfying distributions are possible. We provide probabilistic semantics corresponding to the classical complete and stable semantics and show how labeling schemes provide a bridge from distributions back to argument labelings. In relation to existing work on probabilistic argumentation, we present a taxonomy of semantic notions. Enabled by the constraint-based approach, standard reasoning problems for probabilistic semantics can be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation.

PDF Details DOI

KR Conference 2021 Conference Paper

Admissibility in Probabilistic Argumentation

Christel Baier
Martin Diller
Clemens Dubslaff
Sarah Alice Gaggl
Holger Hermanns
Nikolai Käfer

Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics, and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion in the classical setting, it has been merely reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of argumentation based on probabilistic notions of admissibility in a way that they form fully conservative extensions of classical notions. In particular, our building blocks are not the beliefs regarding single arguments. Instead we start from the fairly natural idea that whatever argumentation semantics is to be considered, semantics systematically induces constraints on the joint probability distribution on the sets of arguments. In some cases there might be many such distributions, even infinitely many ones, in other cases there may be one or none. Standard semantic notions are shown to induce such sets of constraints, and so do their probabilistic extensions. This allows them to be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation. We present a taxonomy of semantic notions, also in relation to published work, together with a running example illustrating our achievements.

PDF Details DOI

AAAI Conference 2017 Conference Paper

Solving Advanced Argumentation Problems with Answer-Set Programming

Gerhard Brewka
Martin Diller
Georg Heissenberger
Thomas Linsbichler
Stefan Woltran

Powerful formalisms for abstract argumentation have been proposed. Their complexity is often located beyond NP and ranges up to the third level of the polynomial hierarchy. The combined complexity of Answer-Set Programming (ASP) exactly matches this complexity when programs are restricted to predicates of bounded arity. In this paper, we exploit this coincidence and present novel efﬁcient translations from abstract dialectical frameworks (ADFs) and GRAPPA to ASP. We also empirically compare our approach to other systems for ADF reasoning and report promising results.

PDF Details

IJCAI Conference 2015 Conference Paper

An Extension-Based Approach to Belief Revision in Abstract Argumentation

Martin Diller
Adrian Haret
Thomas Linsbichler
Stefan R
uuml; mmele
Stefan Woltran

Argumentation is an inherently dynamic process. Consequently, recent years have witnessed tremendous research efforts towards an understanding of how the seminal AGM theory of belief change can be applied to argumentation, in particular for Dung’s abstract argumentation frameworks (AFs). However, none of the attempts has yet succeeded in handling the natural situation where the revision of an AF is guaranteed to be representable by an AF as well. In this work, we present a generic solution to this problem which applies to many prominent I-maximal argumentation semantics. In order to prove a full representation theorem, we make use of recent advances in both areas of argumentation and belief change. In particular, we utilize the concepts of realizability in argumentation and the notion of compliance as used in Horn revision.

PDF Details