Author name cluster

Tjitze Rienstra

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17 papers

2 author rows

NMR Workshop 2025 Conference Paper

Autoformalisation Answer Set Programs for Scheduling Problems using Few-Shot Learning and Chain-of-Thought

Jesse Heyninck
Bart van Gool
Stefano Bromuri
Tjitze Rienstra

Large language models (LLMs) have caused a veritable revolution in the field of AI. However, LLMs do come with some considerable caveats including the lack of logical reasoning ability. This can make it challenging to use LLMs in environments where they need to give reliably correct answers. Recently, attempts have been made to alleviate this concern by generating a more transparent way of solving the problem using an LLM, instead of solving the problem directly with an LLM (so-called autoformalisation). Among others, answer set programs have been tried as a problem-solving intermediary in this context. However, current attempts at autoformalisation of answer set programs has been limited to toy examples or single, simple rules. In this work, we investigate the capabilities of LLMs in generating ASP that solve real-world scheduling problems, and identify techniques such as few-shot learning and chain-of-thought as particularly succesful.

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FLAP Journal 2025 Journal Article

Causation and Argumentation

Alexander Bochman
Federico Cerutti
Tjitze Rienstra

Causality is a feature in a socio-economical context rapidly moving towards an ethical use of robust artificial intelligence. The primary link between cau- sation and argumentation, especially in AI, stems from the fundamental role of causality in explanations, as argued in several works in the explainable arti- ficial intelligence literature. In this sense, theories of causation naturally sug- gest themselves as an essential component of explainable artificial intelligence. Causality also directly supports what-if and counterfactual reasoning, funda- mental components for fair, robust, and resilient use of artificial intelligence tools and systems. Because of its connection with the enquiry, persuasion, and negotiation monologues and dialogues, this article popularizes the fundamental concepts of causality for the computational argumentation research community. It also accounts for the approaches to address research questions at the heart of both argumentation and causality communities, including the connections between causal models and formal argumentation approaches.

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FLAP Journal 2025 Journal Article

Reasoning Alignment for Agentic AI: Argumentation, Belief Revision, and Dialogue

Tjitze Rienstra
Leendert van der Torre
Liuwen Yu

Agentic AI—deployed as technical systems that perceive, decide, and act via tools—faces requirements of safety, accountability, controlled adaptivity, and compositionality. We develop Reasoning Alignment Diagrams (RADs), com- mutative reasoning representations that align a source specification with an argumentation-based explanation route. As illustrative examples, we first show that full-meet belief base revision admits an exact representation within base argumentation via a restricted-attack construction: the revised base equals the intersection of the premises appearing in all stable extensions of the modified framework. This yields a RAD from input to sanctioned output that doubles as an explanation engine. We then compose the “listen” (revision) and “assert” (inference) RADs to model dialogue among agents, enabling explainable and auditable autonomy. Although our results are entirely symbolic, the RAD tem- plate can serve as a specification layer even when other components are opaque or learned. The approach realizes core themes of Gabbay’s programme—logic as a toolbox, combining logics, and argumentation as a host formalism—and supports a principle-based analysis of correctness, transparency, and modular- ity.

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FLAP Journal 2025 Journal Article

The Semantical Structure of Conditionals, and its Relation to Formal Argumentation

Jesse Heyninck
Gabriele Kern-Isberner
Tjitze Rienstra
Kenneth Skiba
Matthias Thimm

Conditionals, i. e. expressions of the logical form “if A, then B”, have been a central topic of study ever since logic was on the academic menu. In contem- porary logic, there is a consensus that the semantics of conditionals are best obtained by stipulating a subset of possible worlds in which the antecedent is true, and verifying whether the consequent is true in those worlds. Such a subset of possible worlds can represent, for example, the most typical worlds in which the antecedent is true. This idea has proven a fruitful basis, allowing for many systematic characterisation results as well as for making connections to other topics, such as belief revision and modal logic. In formal argumentation, the potential of these semantical ideas has not gone unnoticed in the last years, and

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AIJ Journal 2023 Journal Article

Revision, defeasible conditionals and non-monotonic inference for abstract dialectical frameworks

Jesse Heyninck
Gabriele Kern-Isberner
Tjitze Rienstra
Kenneth Skiba
Matthias Thimm

For propositional beliefs, there are well-established connections between belief revision, defeasible conditionals, and nonmonotonic inference. In argumentative contexts, such connections have not yet been investigated. On the one hand, the exact relationship between formal argumentation and nonmonotonic inference relations is a research topic that keeps on eluding researchers despite recently intensified efforts, whereas argumentative revision has been studied in numerous works during recent years. In this paper, we show that relationships between belief revision, defeasible conditionals, and nonmonotonic inference similar to those in propositional logic hold in argumentative contexts as well. We first define revision operators for abstract dialectical frameworks, and use such revision operators to define dynamic conditionals by means of the Ramsey test. We show that such conditionals can be equivalently defined using a total preorder over three-valued interpretations, and study the inferential behaviour of the resulting conditional inference relations.

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AAAI Conference 2022 Conference Paper

Conditional Abstract Dialectical Frameworks

Jesse Heyninck
Matthias Thimm
Gabriele Kern-Isberner
Tjitze Rienstra
Kenneth Skiba

Abstract dialectical frameworks (in short, ADFs) are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. This idea is generalized by letting acceptance conditions being assigned to complex formulas, resulting in conditional abstract dialectical frameworks (in short, cADFs). We define the semantics of cADFs in terms of a non-truth-functional four-valued logic, and study the semantics in-depth, by showing existence results and proving that all semantics are generalizations of the corresponding semantics for ADFs.

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IJCAI Conference 2022 Conference Paper

Possibilistic Logic Underlies Abstract Dialectical Frameworks

Jesse Heyninck
Gabriele Kern-Isberner
Tjitze Rienstra
Kenneth Skiba
Matthias Thimm

Abstract dialectical frameworks (in short, ADFs) are one of the most general and unifying approaches to formal argumentation. As the semantics of ADFs are based on three-valued interpretations, we ask which monotonic three-valued logic allows to capture the main semantic concepts underlying ADFs. We show that possibilistic logic is the unique logic that can faithfully encode all other semantical concepts for ADFs. Based on this result, we also characterise strong equivalence and introduce possibilistic ADFs.

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KR Conference 2021 Short Paper

Distinguishability in Abstract Argumentation

Isabelle Kuhlmann
Tjitze Rienstra
Lars Bengel
Kenneth Skiba
Matthias Thimm

In abstract argumentation, the admissible semantics can be said to distinguish the preferred semantics in the sense that argumentation frameworks with the same admissible extensions also have the same preferred extensions. In this paper we present an exhaustive study of such distinguishability relationships, including those between sets of semantics. We further examine restricted classes of argumentation frameworks, such as self-attack-free and acyclic frameworks. We discuss the relevance of our results in the context of the argumentation framework elicitation problem.

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IJCAI Conference 2021 Conference Paper

Ranking Extensions in Abstract Argumentation

Kenneth Skiba
Tjitze Rienstra
Matthias Thimm
Jesse Heyninck
Gabriele Kern-Isberner

Extension-based semantics in abstract argumentation provide a criterion to determine whether a set of arguments is acceptable or not. In this paper, we present the notion of extension-ranking semantics, which determines a preordering over sets of arguments, where one set is deemed more plausible than another if it is somehow more acceptable. We obtain extension-based semantics as a special case of this new approach, but it also allows us to make more fine-grained distinctions, such as one set being "more complete'' or "more admissible'' than another. We define a number of general principles to classify extension-ranking semantics and develop concrete approaches. We also study the relation between extension-ranking semantics and argument-ranking based semantics, which rank individual arguments instead of sets of arguments.

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KR Conference 2021 Conference Paper

Revision and Conditional Inference for Abstract Dialectical Frameworks

Jesse Heyninck
Gabriele Kern-Isberner
Tjitze Rienstra
Kenneth Skiba
Matthias Thimm

For propositional beliefs, there are well-established connections between belief revision, defeasible conditionals and nonmonotonic inference. In argumentative contexts, such connections have not yet been investigated. On the one hand, the exact relationship between formal argumentation and nonmonotonic inference relations is a research topic that keeps on eluding researchers despite recently intensified efforts, whereas argumentative revision has been studied in numerous works during recent years. In this paper, we show that similar relationships between belief revision, defeasible conditionals and nonmonotonic inference hold in argumentative contexts as well. We first define revision operators for abstract dialectical frameworks, and use such revision operators to define dynamic conditionals by means of the Ramsey test. We show that such conditionals can be equivalently defined using a total preorder over three-valued interpretations, and study the inferential behaviour of the resulting conditional inference relations.

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KR Conference 2020 Conference Paper

Concept Contraction in the Description Logic EL

Tjitze Rienstra
Claudia Schon
Steffen Staab

In this paper we study the problem of concept contraction for the description logic EL. Concept contraction is concerned with the following question: Given two concepts C and D (with the interesting case being that D subsumes C) how can we find a generalisation of C that is not subsumed by D but is otherwise as similar as possible to C? We take an AGM-style approach and model this problem using the notion of a concept contraction operator. We consider constructive definitions as well as sets of postulates for concept contraction, and link the two by means of representation theorems.

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KR Conference 2020 Conference Paper

Independence and D-separation in Abstract Argumentation

Tjitze Rienstra
Matthias Thimm
Kristian Kersting
Xiaoting Shao

We investigate the notion of independence in abstract argumentation, i. e. , the question of whether the evaluation of one set of arguments is independent of the evaluation of another set of arguments, given that we already know the status of a third set of arguments. We provide a semantic definition of this notion and develop a method to discover independencies based on transforming an argumentation framework into a DAG on which we then apply the well-known d-separation criterion. We also introduce the SCC Markov property for argumentation semantics, which generalises the Markov property from the classical acyclic case and guarantees the soundness of our approach.

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IJCAI Conference 2019 Conference Paper

Ranked Programming

Tjitze Rienstra

While probabilistic programming is a powerful tool, uncertainty is not always of a probabilistic kind. Some types of uncertainty are better captured using ranking theory, which is an alternative to probability theory where uncertainty is measured using degrees of surprise on the integer scale from 0 to ∞. In this paper we combine probabilistic programming methodology with ranking theory and develop a ranked programming language. We use the Scheme programming language a basis and extend it with the ability to express both normal and exceptional behaviour of a model, and perform inference on such models. Like probabilistic programming, our approach provides a simple and flexible way to represent and reason with models involving uncertainty, but using a coarser grained and computationally simpler kind of uncertainty.

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KR Conference 2018 Conference Paper

Probabilistic Abstract Argumentation based on SCC Decomposability

Tjitze Rienstra
Matthias Thimm
Beishui Liao
Leendert van der Torre

In this paper we introduce a new set of general principles for probabilistic abstract argumentation. The main principle is a probabilistic analogue of SCC decomposability, which ensures that the probabilistic evaluation of an argumentation framework complies with the probabilistic (in)dependencies implied by the graph topology. We introduce various examples of probabilistic semantics and determine which principles they satisfy. Our work also provides new insights into the relationship between abstract argumentation and the theory of Bayesian networks.

ECAI Conference 2014 Conference Paper

Abduction and Dialogical Proof in Argumentation and Logic Programming

Richard Booth 0001
Dov M. Gabbay
Souhila Kaci
Tjitze Rienstra
Leendert W. N. van der Torre

We develop a model of abduction in abstract argumentation, where changes to an argumentation framework act as hypotheses to explain the support of an observation. We present dialogical proof theories for the main decision problems (i. e. , finding hypotheses that explain skeptical/credulous support) and we show that our model can be instantiated on the basis of abductive logic programs.

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IJCAI Conference 2013 Conference Paper

Opponent Models with Uncertainty for Strategic Argumentation

Tjitze Rienstra
Matthias Thimm
Nir Oren

This paper deals with the issue of strategic argumentation in the setting of Dung-style abstract argumentation theory. Such reasoning takes place through the use of opponent models—recursive representations of an agent’s knowledge and beliefs regarding the opponent’s knowledge. Using such models, we present three approaches to reasoning. The first directly utilises the opponent model to identify the best move to advance in a dialogue. The second extends our basic approach through the use of quantitative uncertainty over the opponent’s model. The final extension introduces virtual arguments into the opponent’s reasoning process. Such arguments are unknown to the agent, but presumed to exist and interact with known arguments. They are therefore used to add a primitive notion of risk to the agent’s reasoning. We have implemented our models and we have performed an empirical analysis that shows that this added expressivity improves the performance of an agent in a dialogue.

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JELIA Conference 2012 Conference Paper

Building an Epistemic Logic for Argumentation

François Schwarzentruber
Srdjan Vesic
Tjitze Rienstra

Abstract In this paper, we study a multi-agent setting in which each agent is aware of a set of arguments. The agents can discuss and persuade each other by putting forward arguments and counter-arguments. In such a setting, what an agent will do, i. e. what argument she will utter, may depend on what she knows about the knowledge of other agents. For example, an agent does not want to put forward an argument that can easily be attacked, unless she believes that she is able to defend her argument against possible attackers. We propose a logical framework for reasoning about the sets of arguments owned by other agents, their knowledge about other agents’ arguments, etc. We do this by defining an epistemic logic for representing their knowledge, which allows us to express a wide range of scenarios.

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