Despite the increasing interest in the reasoning abilities of Large Language Models (LLMs), existing work shows limitations in assessing logic abilities independently from lexical memory. We address this gap with Mystery-Zebra. This robust two-part benchmark (4,290 puzzles) challenges the logic abstraction abilities of LLMs in two setups: (1) a lexical obfuscation setup tests the dependence of LLMs on lexical content based on two canonical grid puzzles widely spread on the Internet; (2) a set of new grid puzzles in 42 different sizes and 12 difficulty levels tests how the formal difficulty degree of a puzzle affects LLMs.We test open and closed-weight LLMs on both parts of the benchmark. The results on part two suggest that model sizes up to 70B parameters have only a minor influence when solving newly generated puzzles, while performance mainly relates to the number of items in the puzzle. The results on the first part of the benchmark suggest that the applied obfuscation strategies help to mitigate effects of logic puzzles being part of LLM training data, showing a drastic drop in performance for obfuscated versions of well-known puzzles. In addition we conduct a case-study on the first part of the benchmark predicting the position of single items, unveiling that the reasoning abilities of LLMs are mainly limited to a few consecutive steps of reasoning.

Argument Mining (AM) involves the automatic identification of argument structure in natural language. Traditional AM methods rely on micro-structural features derived from the internal properties of individual Argumentative Discourse Units (ADUs). However, argument structure is shaped by a macro-structure capturing the functional interdependence among ADUs. This macro-structure consists of segments, where each segment contains ADUs that fulfill specific roles to maintain coherence within the segment (**local coherence**) and across segments (**global coherence**). This paper presents an approach that models macro-structure, capturing both local and global coherence to identify argument structures. Experiments on heterogeneous datasets demonstrate superior performance in both in-dataset and cross-dataset evaluations. The cross-dataset evaluation shows that macro-structure enhances transferability to unseen datasets.

Traditionally, argument mining research has approached the task of automatic identification of argument structures by using existing definitions of what constitutes an argument, while leaving the equally important matter of what does not qualify as an argument unaddressed. With the ability to distinguish between what is and what is not a natural language argument being at the core of argument mining as a field, it is interesting that no previous work has explored approaches to effectively select non-related propositions (i.e., propositions that are not connected through an argumentative relation, such as support or attack) that improve the data for learning argument mining tasks better. In this paper, we address the question of how to effectively sample non-related propositions from six different argument mining corpora belonging to different domains and encompassing both monologue and dialogue forms of argumentation. To that end, in addition to considering undersampling baselines from previous work, we propose three new sampling strategies relying on context (i.e., short/long) and the semantic similarity between propositions. Our results indicate that using more informed sampling strategies improves the performance, not only when evaluating models on their respective test splits, but also in the case of cross-domain evaluation.

Accurate forecasting of future events is critical across domains such as finance, policymaking, and intelligence analysis. In this work we investigate the role of argumentative reasoning in successful forecasting. Drawing on theories of argumentation, we perform a fine-grained annotation of forecasts and their supporting rationales from a major forecasting competition, identifying argumentative structures and discourse moves. We derive a variety of structural and textual features from the resulting argument graphs, such as argumentative balance, support, and conflict. We perform a correlation analysis to examine the relationship of individual features to forecasting success, and apply a Graph Neural Network (GNN) to learn from these structures, enriching the node representations with text embeddings. Our results show that balanced argumentative coverage has a statistically significant correlation with forecasting success, and that models incorporating argument graphs and text embeddings outperform baselines that use surface text alone when predicting forecasting success. To our knowledge, this is the first study to systematically link fine-grained argumentative structure and graph-based reasoning to forecasting accuracy, offering novel insights into strategies that support more accurate predictions of future events. By identifying key argumentative features that correlate with forecasting success, this work provides valuable guidance for developing tools and strategies to improve forecasting accuracy, with applications across multiple decision-making domains.

In computational argument mining, the final constructed argument graph is usually the center of attention, with researchers analyzing its structure and developing methods to extract valuable insights from it. In this paper, we argue that analyzing only the final argument graph is not enough. We demonstrate that the same argument graphs do not imply the same argument construction styles (ways of populating them), which are an important abstract argumentation concept. Therefore, both final structure and the construction style should be considered when analyzing arguments. To address the analysis of argument construction styles, we present the DRACS: Diachronic Representation of Argument Construction Styles framework to capture argument construction style features and enable their analysis alongside the graph structures. We evaluate our methodology on three distinct datasets of pre-defined similar and differing construction styles: the AIF-labeled corpora UNSC and US2016, as well as a custom corpus of synthetic tree-based argument graphs. We apply our method to highlight similarities and differences of argument construction styles on US2016 datasets. Our findings suggest that even if the final argument graphs display a high degree of similarity, their construction styles could differ significantly.

Abstract Argument relation classification (ARC) between argument components (ACs) has made significant progress in recent years. However, many existing approaches either rely heavily on external knowledge or on linguistic information encoded in Pre-trained Language Models (PLMs) or large language models, often neglecting the extraction of fine-grained, semantic information within ACs. This information is essential for developing strategies tailored to the specific challenges of ARC tasks. To address this, we propose leveraging Frame Semantic Parsing (FSP), an open-source transformer, to extract semantic frames. These frames, consisting of triggers and arguments along with their roles, represent the semantic relationships within ACs. We then design two types of prompt templates: one for triggers and arguments, and another for frames and roles, to generate conceptual information that facilitates ARC. Finally, we utilize the RoBERTa PLM model, training it with the two types of prompt templates using a Siamese network architecture, which encodes two inputs separately, with multi-head attention. Extensive experiments across six domain-specific argument mining datasets demonstrate that our FSP–ARC approach yields competitive results compared to four state-of-the-art baselines in terms of accuracy, precision, recall, and macro Macro F1s.

It is known from large-scale crowd experimentation that some people are innately better at analysing complex situations and making justified predictions – the so-called ‘superforecasters’. Surprisingly, however, there has to date been no work exploring the role played by the reasoning in those justifications. Bag-of-words analyses might tell us something, but the real value lies in understanding what features of reasoning and argumentation lead to better forecasts – both in providing an objective measure for argument quality, and even more importantly, in providing guidance on how to improve forecasting performance. The work presented here covers the creation of a unique dataset of such prediction rationales, the structure of which naturally lends itself to partially automated annotation which in turn is used as the basis for subsequent manual enhancement that provides a uniquely fine-grained and close characterisation of the structure of argumentation, with potential impact on forecasting domains from intelligence analysis to investment decision-making.

The dearth of literature combining hypothesis-making and collaborative problem solving presents a problem in the investigation into how hypotheses are generated in group environments. A new dataset, the Resolving Investigative hyPotheses (RIP) corpus, is introduced to address this issue. The corpus uses the fictionalised environment of a murder investigation game. An artificial environment restricts the number of possible hypotheses compared to real-world situations, allowing a deeper dive into the data. In three groups of three, participants collaborated to solve the mystery: two groups came to the wrong conclusion in different ways, and one succeeded in solving the game. RIP is a 49k-word dialogical corpus, consisting of three sub-corpora, annotated for argumentation and discourse structure on the basis of Inference Anchoring Theory. The corpus shows the emergent roles individuals took on and the strategies the groups employed, showing what can be gained through a deeper exploration of this domain. The corpus bridges the gap between these two areas – hypothesis generation and collaborative problem solving – by using an environment rich with potential for hypothesising within a highly collaborative space.

This demo paper introduces an application that is capable of identifying and visualising diagnostic arguments within abstract argumentation systems. The software presented is underpinned by a novel algorithm, called the Diagnostic Argument Identifier, that combines a semantic-based approach with a technique from the information-theoretic literature, to quantify the impact that the removal of an argument has on the acceptability of other arguments.

Identifying the smallest units of human argumentation remains a key challenge for computational models of argument. This study tested the assumption that argumentative discourse units (ADUs) can be best described as clauses. Two online experiments investigated the role of ADUs in human language processing (Experiment 1) and recall (Experiment 2), providing evidence that discourse comprehension might be influenced by syntactic depth. Experiment 1 analysed participants’ cued recall of pairs of clauses to identify whether they relied on clausal units when encoding information. Experiment 2 tested effects of manipulating the syntactic complexity (natural language – varying complexity, elementary language – one clause per sentence, or atomic language – one sub-clausal unit of information per sentence) on participants’ free recall of short encyclopaedic entries adapted from Wikipedia. Both experiments found small-to-medium effects suggesting that defining ADUs as clauses might be justified to a degree, with potential implications for computational models of human argumentation.

Argument mining (AM) involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs).The essence of ARs among ADUs is contextdependent and lies in maintaining a coherent flow of ideas, often centered around the relations between discussed entities, topics, themes or concepts.However, these relations are not always explicitly stated; rather, inferred from implicit chains of reasoning connecting the concepts addressed in the ADUs.While humans can infer such background knowledge, machines face challenges when the contextual cues are not explicitly provided.This paper leverages external resources, including WordNet, ConceptNet, and Wikipedia to identify semantic paths (knowledge paths) connecting the concepts discussed in the ADUs to obtain the implicit chains of reasoning.To effectively leverage these paths for AR prediction, we propose attention-based Multi-Network architectures.Various architecture are evaluated on the external resources, and the Wikipedia based configuration attains F-scores of 0.85, 0.84, 0.70, and 0.87, respectively, on four diverse datasets, showing strong performance over the baselines.

Following the establishment in 2006 of a representational standard for the computational handling of structures of argumentation, the Argument Interchange Format, it became possible to develop a vision for the coherent integration of multifarious services, components and tools that create, consume, navigate, analyse, evaluate and manipulate arguments and debates. This vision was the Argument Web with theoretical foundations laid by Rahwan et al. (2007), and practical engineering work described by Bex et al. (2013). Over the intervening period, the key challenge has been to demonstrate the practical and societal value of the Argument Web by taking its tools and applications to larger audiences. This paper lays out three approaches by which the Argument Web has been scaled up in this way, each in partnership with the BBC, and each with different kinds of evaluation and impact. Transitioning these technologies to large user groups paves the way for broader-scale uptake of the Argument Web and heralds the translation from lab to real-world application for a substantial research community working in argument technology.

We present a method of annotating very large arguments, with the use of IMC-Tool. IMC-Tool aids in creating long-distance argument structure relations, by providing a simple annotation tool, and integration software to synthesise argument annotations at scale.

In this paper, we present Polemicist 2, a dialogical interface for exploring complex debates from the BBC Radio 4 programme The Moral Maze. Polemicist allows the user to interact with software agents representing the participants in the original programme. The software enables the user to explore the topic as they wish, asking questions to dive deeper on the areas that interest them most.

Broadcast political debate is a core pillar of democracy: it is the public’s easiest access to opinions that shape policies and enables the general public to make informed choices. With QT30, we present the largest corpus of analysed dialogical argumentation ever created (19,842 utterances, 280,000 words) and also the largest corpus of analysed broadcast political debate to date, using 30 episodes of BBC’s ‘Question Time’ from 2020 and 2021. Question Time is the prime institution in UK broadcast political debate and features questions from the public on current political issues, which are responded to by a weekly panel of five figures of UK politics and society. QT30 is highly argumentative and combines language of well-versed political rhetoric with direct, often combative, justification-seeking of the general public. QT30 is annotated with Inference Anchoring Theory, a framework well-known in argument mining, which encodes the way arguments and conflicts are created and reacted to in dialogical settings. The resource is freely available at http://corpora.aifdb.org/qt30.

While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between two statements, namely, (i) factual consistency, (ii) sentiment coherence, (iii) causal relation, and (iv) normative relation. We demonstrate that our operationalization of these logical mechanisms classifies argumentative relations without directly training on data labeled with the relations, significantly better than several unsupervised baselines. We further demonstrate that these mechanisms also improve supervised classifiers through representation learning.

Finding attackable sentences in an argument is the first step toward successful refutation in argumentation.We present a first large-scale analysis of sentence attackability in online arguments.We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences.We demonstrate that a sentence's attackability is associated with many of these characteristics regarding the sentence's content, proposition types, and tone, and that an external knowledge source can provide useful information about attackability.Building on these findings, we demonstrate that machine learning models can automatically detect attackable sentences in arguments, significantly better than several baselines and comparably well to laypeople. 1

Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives.These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly.However, most argument mining systems and computational linguistics research have paid little attention to implicitly asserted propositions in argumentation.In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation.By evaluating the models on a corpus of 2016 U.S. presidential debates and online commentary, we demonstrate the effectiveness and limitations of the computational models.Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation. 1

We introduce a corpus of the 2016 U.S. presidential debates and commentary, containing 4,648 argumentative propositions annotated with fine-grained proposition types. Modern machine learning pipelines for analyzing argument have difficulty distinguishing between types of propositions based on their factuality, rhetorical positioning, and speaker commitment. Inability to properly account for these facets leaves such systems inaccurate in understanding of fine-grained proposition types. In this paper, we demonstrate an approach to annotating for four complex proposition types, namely normative claims, desires, future possibility, and reported speech. We develop a hybrid machine learning and human workflow for annotation that allows for efficient and reliable annotation of complex linguistic phenomena, and demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates. This new dataset and method can support technical researchers seeking more nuanced representations of argument, as well as argumentation theorists developing new quantitative analyses.

This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure.The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects.A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects.Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects.The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations.Propositions are connected iteratively to form a graph structure.The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.

Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

This paper briefly describes AIF-EL, an OWL2-EL compliant ontology for the Argument Interchange Format.

In this paper, we present Test Your Argument, part of a suite of argument technology piloted in conjunction with BBC programming. Test Your Argument offers users the opportunity to interact with real arguments taken from the BBC Radio 4 programme Moral Maze. Users are guided through different aspects of strengthening and critiquing an argument as well as considering both sides of the issue under discussion. Since December 2017, Test Your Argument has had over 10,000 visitors.

We demonstrate the first prototype of the ADD-up visual analytics system. The Augmented Deliberative Democracy (ADD-up) project aims to enhance public deliberations by providing argument analytics in real time. The system will ultimately take a stenographic feed of a public deliberation meeting, automatically extract the arguments therein and project visual analytics intended to improve the deliberative quality of the event.

In this paper, we present an in-depth comparative analysis of two classifications of argument schemes: Walton's typology and Wagemans' Periodic Table of Arguments. We describe annotation guidelines for each classification and apply these to a corpus of arguments from the 2016 US presidential debates. In so doing, we achieve substantial inter-annotator agreement, and produce what, to the best of our knowledge, are the two largest and most reliably annotated corpora of argument schemes in dialogical argumentation publicly available. In describing the creation and comparison of these corpora, we discuss the strengths of each, with an eye towards both computational modelling and argument mining.

The simulation of mathematical reasoning has been a driving force throughout the history of Artificial Intelligence research. However, despite significant successes in computer mathematics, computers are not widely used by mathematicians apart from their quotidian applications. An oft-cited reason for this is that current computational systems cannot do mathematics in the way that humans do. We draw on two areas in which Automated Theorem Proving (ATP) is currently unlike human mathematics: firstly in a focus on soundness, rather than understandability of proof, and secondly in social aspects. Employing techniques and tools from argumentation to build a framework for mixed-initiative collaboration, we develop three complementary arcs. In the first arc – our theoretical model – we interpret the informal logic of mathematical discovery proposed by Lakatos, a philosopher of mathematics, through the lens of dialogue game theory and in particular as a dialogue game ranging over structures of argumentation. In our second arc – our abstraction level – we develop structured arguments, from which we induce abstract argumentation systems and compute the argumentation semantics to provide labelings of the acceptability status of each argument. The output from this stage corresponds to a final, or currently accepted proof artefact, which can be viewed alongside its historical development. Finally, in the third arc – our computational model – we show how each of these formal steps is available in implementation. In an appendix, we demonstrate our approach with a formal, implemented example of real-world mathematical collaboration. We conclude the paper with reflections on our mixed-initiative collaborative approach.

Understanding the dynamics of argumentation systems is a crucial component in the development of computational models of argument that are used as representations of belief. To that end, in this article, we introduce a model of Argument Revision, presented in terms of the contraction and revision of a system of structured argumentation. Argument Revision is influenced by the AGM model of belief revision, but with certain key differences. First, Argument Revision involves modifying the underlying model (system of argumentation) from which beliefs are derived, allowing for a finer-grained approach to modifying beliefs. Secondly, the richer structure provided by a system of argumentation permits a determination of minimal change based on quantifiable effects on the system as opposed to qualitative criteria such as entrenchment orderings. Argument Revision does, however, retain a close link to the AGM approach to belief revision. A basic set of postulates for rational revisions and contractions in Argument Revision is proposed; these postulates are influenced by, and capture the spirit of, those found in AGM belief revision. After specifying a determination of minimal change, based on measurable effects on the system, we conclude the article by going on to show how Argument Revision can be used as a strategic tool by a participant in a multi-agent dialogue, assisting with commitment retraction and dishonesty. In systems of argumentation that contain even small knowledge bases, it is difficult for a dialogue participant to fully assess the impact of seemingly trivial changes to that knowledge base, or other parts of the system; we demonstrate, by means of an example, that Argument Revision solves this problem through a determination of minimal change that is justifiable and intuitive.

Governments around the world are increasingly utilising online platforms and social media to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.

Debating technologies, a newly emerging strand of research into computational technologies to support human debating, offer a powerful way of providing naturalistic, dialogue-based interaction with complex information spaces. The full potential of debating technologies for dialogical argument can, however, only be realized once key technical and engineering challenges are overcome, namely data structure, data availability, and interoperability between components. Our aim in this article is to show that the Argument Web, a vision for integrated, reusable, semantically rich resources connecting views, opinions, arguments, and debates online, offers a solution to these challenges. Through the use of a running example taken from the domain of citizen dialogue, we demonstrate for the first time that different Argument Web components focusing on sensemaking, engagement, and analytics can work in concert as a suite of debating technologies for rich, complex, dialogical argument.

Despite the fact it has been recognised since Aristotle that ethos and credibility play a critical role in many types of communication, these facts are rarely studied in linguistically oriented AI which has enjoyed such success in processing complex features as sentiment, opinion, and most recently arguments. This paper shows how a text analysis pipeline of structural and statistical approaches to natural language processing (NLP) can be deployed to tackle ethos by mining linguistic resources from the political domain. We summarise a coding scheme for annotating ethotic expressions; present the first openly available corpus to support further, comparative research in the area; and report results from a system for automatically recognising the presence and polarity of ethotic expressions. Finally, we hypothesise that in the political sphere, ethos analytics – including recognising who trusts whom and who is attacking whose reputation – might act as a powerful toolset for understanding and even anticipating the dynamics of governments. By exploring several examples of correspondence between ethos analytics in political discourse and major events and dynamics in the political landscape, we uncover tantalising evidence in support of this hypothesis.

We propose a dialogue game for mediation and its formalization in DGDL. This dialectical system is available as software through Arvina for automatic execution. This work expands the literature in dialectical systems, in particular those for more than two players, and shows the practical impact on mediation activity through the opportunity offered to mediators once implemented.

Rapid growth in the area of argument mining has resulted in an ever increasing volume of analysed argument data. Being able to store information about arguments people make in favour or against different opinions, decisions and actions is a highly valuable resource, yet extremely challenging for sense-making. How, for example, can an analyst quickly check whether in a corpus of citizen dialogue people tend to rather agree or disagree with new policies proposed by the department of transportation; how can she get an insight into the interactions typical of this specific dialogical context; how can the general public easily see which presidential candidate is currently winning the debate by being able to successfully defend his arguments? In this paper, we propose Argument Analytics – a suite of techniques which provide interpretation of, and insight into, large-scale argument data for both specialist and general audiences.

Argumentation schemes are patterns of human reasoning which have been detailed extensively in philosophy and psychology. In this paper we demonstrate that the structure of such schemes can provide rich information to the task of automatically identify complex argumentative structures in natural language text. By training a range of classifiers to identify the individual proposition types which occur in these schemes, it is possible not only to determine where a scheme is being used, but also the roles played by its component parts. Furthermore, this task can be performed on segmented natural language, with no prior knowledge of the text's argumentative structure.

The constant emergence and change of current technologies in the form of digital products and services can cause certain groups of the population to feel excluded. Older adults represent one such group. Our research combines computational models of argument and human-centric computing to impact the way in which older adults interact with broadcast debates. We present a preliminary user study where older adults interact with a debate and propose an application which uses speech recognition to classify spoken utterances and related them to segmented debates. Moreover, we discuss preliminary results on older adults interacting with the application in pilot experiments.

In this paper, we present the Dialogue Game Execution Platform (DGEP), which is able to process and execute any dialogue game specified in a general description language and build arguments and dialogue histories in the language of the AIF ontology. Thus, DGEP allows us to generalise techniques for generation and investigation of dialogues and, through a set of web services, connect a wide variety of multi-agent systems and human-computer interfaces for dialogue.

Argument mining has started to yield early results in automatic analysis of text to produce representations of reason-conclusion structures. This paper addresses for the first time the question of automatically extracting such structures from dialogical settings of argument. More specifically, we introduce theoretical foundations for dialogical argument mining as well as show the initial implementation in a software for dialogue processing, and the application in corpus analysis. We combine analysis of illocutionary structure with structured argumentation frameworks as our scaffolding, and apply a combination of statistical and grammatically based analytical techniques.

This paper introduces OVA+, an on-line interface for the analysis of arguments. It is the result of an attempt to provide a tool relying on the Argument Interchange Format theory and Inference Anchoring Theory schemes.

This paper introduces AIFdb Corpora, an addition to AIFdb for collecting and presenting sets of AIF argument maps. AIFdb Corpora allows any user to create a corpus and share the contents.

The constant emergence and change of technologies in the form of digital products and services can cause certain groups of the population to feel excluded, older adults represent one such group. We investigate how to combine applied research on computational models of argument and human-centric computing to impact the way in which older adults interact with broadcast debates. This paper describes a technology application that uses a speech recognition interface to interact with broadcast debates. The application classifies spoken utterances and creates positive or negative “votes” related arguments from a debate. We describe a user study where older adults interact with a debate using the application. Our results indicate that the use of speech recognition, extra information provided to users, and feedback on their interaction, plays an important role in their engagement with the debate.

We present a dialogue game representation of Lakatos's theory of justification and discovery in mathematics and describe our implementation of the game. As well as demonstrating a new domain for dialogue games, this could provide the basis for systems which can be used to aid mathematicians in their work.

In this paper, we present a software tool for ‘ArguBlogging’, which allows users to construct debate and discussions across blogs, linking existing and new online resources to form distributed, structured conversations. Arguments and counterarguments can be posed by giving opinions on one’s own blog and replying to other bloggers’ posts. The resulting argument structure is connected to the Argument Web, in which argumentative structures are made semantically explicit and machine-processable. We discuss the ArguBlogging tool and the underlying infrastructure and ontology of the Argument Web.

The Argument Interchange Format (AIF) has been devised in order to support the interchange of ideas and data between different projects and applications in the area of computational argumentation. In order to support such interchange, an abstract ontology for argumentation is presented, which serves as an interlingua between various more concrete argumentation languages. In this article, we aim to give what is essentially a logical specification of the AIF ontology by mapping the ontology onto the logical ASPIC+ framework for argumentation. We thus lay foundations for interrelating formal logic-based approaches to argumentation captured by the ASPIC+ framework and the wider class of argumentation languages, including those that are more informal and user-orientated.

We have implemented a translator that translates Carneades Argument Graphs as specified in the LKIF files of the Carneades editor to a database specification of the Argument Interchange Format and vice versa. In this paper the algorithms for this translation are presented.

Dialogue systems attempt to capture structured communication with the aim of understanding, improving, and automatically recreating su communication. This paper discusses dialogue templates: blueprints that can be instantiated and combined to form argumentative dialogues. These templates provide a generic way of representing individual dialogue systems and allow us to generalise techniques for investigation, generation and recognition of dialogues.

The paper proposes a new perspective on modelling ad hominem (AH) techniques in a dialogue. The approach is built upon the following assumptions: (i) that ad hominem is not an inferential, but undercutting structure; (ii) that it can be a non-fallacious dialectical technique in some communicative contexts; and (iii) that critical questions in Walton's AH scheme can be used to determine strategies of defending against AH attack. We aim to achieve two goals in this paper: first, to represent the deep ontological structure of dialogues with the speaker's character attacks and defense; and second, to design a game AdHD in which a personal attack, and defense against it, are legitimate dialogue moves.

In this paper, we present Arvina, an online discussion tool supporting mixed initiative argumentation. Arvina allows stored arguments in the Argument Web to be introduced by software agents which human participants can then interact with.

This paper introduces AIFdb, a database solution for the Argument Web. AIFdb offers an array of web service interfaces allowing a wide range of software to interact with the same argument data.

The field of cancer research is now generating vast amounts of data from a variety of high throughput techniques and these have helped to define cancers based on their genetic foundations. As this knowledge on the processes and underlying genetics of cancer improve, these should be factored back into the research and analyses conducted by other researchers. Managing this volume of data, often conflicting, is becoming increasingly challenging for researchers. This work demonstrates an innovative application of argumentation theory within cancer research by providing a framework to accommodate missing data, address critical questions and generate hypotheses. The prototype system has been validated to demonstrate it identifies the same interesting interactions and molecules as researchers, even when certain key data was deliberately withheld from the system.

In this paper, we present ArguBlogging, a simple tool that allows blog users to directly respond to text on a web page, publishing the response to their blog while simultaneously capturing the argumentative structure in the Argument Web via the Argument Interchange Format.

In this paper, we present TOAST, a system that implements the ASPIC+framework. TOAST accepts a knowledge base and rule set with associated preference and contrariness information, and returns both textual and visual commentaries on the acceptability of arguments in the derived abstract framework.

In order to support the interchange of ideas and data between different projects and applications in the area of computational argumentation, a common ontology for computational argument, the Argument Interchange Format (AIF), has been devised. One of the criticisms levelled at the AIF has been that it does not take into account formal argumentation systems and their associated argumentation-theoretic semantics, which are part of the main focus of the field of computational argumentation. This paper aims to meet those criticisms by analysing the core AIF ontology in terms of the recently developed ASPIC argumentation framework.

Abstract argument frameworks have been used for various applications within multi-agent systems, including reasoning and negotiation. Different argument frameworks make use of different inter-argument relations and semantics to identify some subset of arguments as coherent, yet there is no easy way to map between these frameworks; most commonly, this is done manually according to human intuition. In response, in this paper, we show how a set of arguments described using Dung's or Nielsen's argument frameworks can be mapped from and to an argument framework that includes both attack and support relations. This mapping preserves the framework's semantics in the sense that an argument deemed coherent in one framework is coherent in the other under a related semantics. Interestingly, this translation is not unique, with one set of arguments in the support based framework mapping to multiple argument sets within the attack only framework. Additionally, we show how EAF can be mapped into a subset of the argument interchange format (AIF). By using this mapping, any other argument framework using this subset of AIF can be translated into a DAF while preserving its semantics.

This paper builds upon the proposed dialogical extensions to the AIF, termed AIF+, by making explicit the representation of the role of illocutionary force in the connection between argument structures and dialogical structures. Illocutionary force is realised in the form of Illocutionary Application (YA-) nodes that provide an explicit linkage between the locutions uttered during a dialogue and the underlying arguments expressed by the content of those locutions. This linkage is explored in the context of two contrasting dialogue games from the literature, demonstrating how the approach can support the development of computational models in which the speech-act function of communicative moves can be accounted for.

Software tools for working with argument generally exist as large systems that wrap their entire feature set in the application as a whole. This approach, while perfectly valid, can result in users having to use small parts of multiple systems to carry out a specific task. In this paper, we present a series of web services, that each encapsulate small pieces of functionality for working with argument. We then demonstrate two example systems built by connecting these services in the form of a UNIX-style pipeline.

This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.

This article reviews recent advances in the interdisciplinary area lying between artificial intelligence and the theory of argumentation. The article has two distinct foci: first, examining the ways in which argumentation has inspired new models of logical and computational intelligence, and second, exploring how AI techniques have been used and extended to model and handle real world argument in a wide variety of domains including law, education, medicine, and e-commerce. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1–15, 2007.

This paper analyses the phenomenon of a shift of the burden of proof in legal persuasion dialogues. Some sample dialogues are analysed of types of situations where such a shift may occur, viz. reasoning with defeasible rules, reasoning with argumentation schemes and reasoning with mere presumptions. It is argued that whether a shift in the burden of proof occurs can itself become the subject of dispute and it is shown how a dialogue game protocol for persuasion can be extended to let it regulate persuasion dialogues about the burden of proof. It is also shown that dialogues about the burden of proof are often implicitly about the precise form of the rules used in an argument.

This paper studies the modelling of legal reasoning about evidence within general theories of defeasible reasoning and argumentation. In particular, it is studied how Wigmore's method for charting evidence and its use by modern legal evidence scholars can be exploited by modern visualisation software for argumentation, and how a formal account of the method can be given in terms of logics for defeasible argumentation. Two notions turn out to be crucial, viz. argumentation schemes and empirical generalisations.

This article presents a formal framework and outlines a method that autonomous agents can use to negotiate the semantics of their communication language at run–time. Such an ability is needed in open multi–agent systems so that agents can ensure they understand the implications of the utterances that are being made and so that they can tailor the meaning of the primitives to best fit their prevailing circumstances. To this end, the semantic space framework provides a systematic means of classifying the primitives along multiple relevant dimensions. This classification can then be used by the agents to structure their negotiation (or semantic fixing) process so that they converge to the mutually agreeable semantics that are necessary for coherent social interactions.