Working on the Argument Pipeline: Through Flow Issues between Natural Language Argument, Instantiated Arguments, and Argumentation Frameworks

In many domains of public discourse such as arguments about public policy, there is an abundance of knowledge to store, query, and reason with. To use this knowledge, we must address two key general problems: first, the problem of the knowledge acquisition bottleneck between forms in which the knowledge is usually expressed, e.g., natural language, and forms which can be automatically processed; second, reasoning with the uncertainties and inconsistencies of the knowledge. Given such complexities, it is labour and knowledge intensive to conduct policy consultations, where participants contribute statements to the policy discourse. Yet, from such a consultation, we want to derive policy positions, where each position is a set of consistent statements, but where positions may be mutually inconsistent. To address these problems and support policy-making consultations, we consider recent automated techniques in natural language processing, instantiating arguments, and reasoning with the arguments in argumentation frameworks. We discuss application and “bridge” issues between these techniques, outlining a pipeline of technologies whereby: expressions in a controlled natural language are parsed and translated into a logic (a literals and rules knowledge base), from which we generate instantiated arguments and their relationships using a logic-based formalism (an argument knowledge base), which is then input to an implemented argumentation framework that calculates extensions of arguments (an argument extensions knowledge base), and finally, we extract consistent sets of expressions (policy positions). The paper reports progress towards reasoning with web-based, distributed, collaborative, incomplete, and inconsistent knowledge bases expressed in natural language

Argumentation Theory for Mathematical Argument

To adequately model mathematical arguments the analyst must be able to represent the mathematical objects under discussion and the relationships between them, as well as inferences drawn about these objects and relationships as the discourse unfolds. We introduce a framework with these properties, which has been used to analyse mathematical dialogues and expository texts. The framework can recover salient elements of discourse at, and within, the sentence level, as well as the way mathematical content connects to form larger argumentative structures. We show how the framework might be used to support computational reasoning, and argue that it provides a more natural way to examine the process of proving theorems than do Lamport's structured proofs.Comment: 44 pages; to appear in Argumentatio

Deliberation and Decision Making Online: Evaluating Platform Design

This thesis explores the potential of ICT and online communication to deepen democracy and support large scale online deliberation. It draws together the most promising current practices in online deliberation, presenting a theoretical and empirical exploration of innovative online deliberation platforms. ICT and online communication is increasingly sophisticated and ubiquitous in public life yet its democratic impact is ambiguous. Online engagement is characterised by low quality, disorganised deliberation. Experimental platforms have emerged which utilise novel design, argument visualisation, and machine learning to support large scale deliberation. The fields of informal logic and collective intelligence have been influential on the developments of these platforms. But the platforms and the perspectives that influence them have been neglected by wider research into online deliberation. The thesis seeks to address the question: to what extent can developments in informal logic and collective intelligence address problems in the theory and practice of online deliberation? The theoretical analysis explores the insights that emerge from a comparison of the approaches of informal logic, collective intelligence and deliberative democratic theory. Models of argumentation and reasonableness from collective intelligence and informal logic reveal ways in which deliberative theory is under-defined, as well as providing techniques to structure, support and analyse deliberative processes. The empirical element draws together and analyses the experiences of online deliberation practitioners to provide a deeper understanding of the opportunities and challenges ICT presents for democracy. These novel technologies indicate how challenges associated with knowledge coordination, participant behaviour and information overload can be ameliorated. Yet analysis of the platforms also identifies resourcing, recruitment, collective attention and the application of AI as barriers to developing effective online deliberative spaces