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

Sketching the vision of the Web of Debates

The exchange of comments, opinions, and arguments in blogs, forums, social media, wikis, and review websites has transformed the Web into a modern agora, a virtual place where all types of debates take place. This wealth of information remains mostly unexploited: due to its textual form, such information is difficult to automatically process and analyse in order to validate, evaluate, compare, combine with other types of information and make it actionable. Recent research in Machine Learning, Natural Language Processing, and Computational Argumentation has provided some solutions, which still cannot fully capture important aspects of online debates, such as various forms of unsound reasoning, arguments that do not follow a standard structure, information that is not explicitly expressed, and non-logical argumentation methods. Tackling these challenges would give immense added-value, as it would allow searching for, navigating through and analyzing online opinions and arguments, obtaining a better picture of the various debates for a well-intentioned user. Ultimately, it may lead to increased participation of Web users in democratic, dialogical interchange of arguments, more informed decisions by professionals and decision-makers, as well as to an easier identification of biased, misleading, or deceptive arguments. This paper presents the vision of the Web of Debates, a more human-centered version of the Web, which aims to unlock the potential of the abundance of argumentative information that currently exists online, offering its users a new generation of argument-based web services and tools that are tailored to their real needs

Finding enthymemes in real-world texts: A feasibility study

Enthymeme reconstruction, i.e. the task of reformulating arguments with missing propositions, is an exciting task at the borderline of text understanding and argument interpretation. However, there is some doubt in the community about the feasibility of this task due to the wide range of possible reformulations that are open to humans. We therefore believe that research on how to define an objective ground truth for these tasks is necessary before any work on the automatic reconstruction can begin. Here, we present a feasibility study for the task of finding and expanding enthymemes involving a fortiori arguments in real-world texts, and we show that given a sufficiently strict reformulation of the human annotation task, substantial agreement can be achieved. We split the task into three sub-tasks: 1. deciding whether a candidate text span really represents an enthymematic argument, 2. classifying the type of a fortiori argument concerned and 3. describing the missing premise in natural language. In a case study involving the two authors of this paper as annotators, we test a specific type of a fortiori arguments, the let alone construction, for its suitability for reaching high agreement in all three stages of the task. We also discuss pragmatic effects of let alone and how they relate to argumentation theory

Online Handbook of Argumentation for AI: Volume 1

This volume contains revised versions of the papers selected for the first volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and Stefan Sarkadi and Andreas Xydi

Negotiating Socially Optimal Allocations of Resources with Argumentation

The resource allocation problem of multi-agent systems is the problem of deciding how to allocate resources, controlled by agents, to agents within a given system. Agents typically have preferences over alternative allocations of resources. These preferences may be derived from the agents’ goals, which can be fulfilled by different plans (sets of resources). The problem arises because agents may not be able to fulfil their goals without being re-allocated resources controlled by other agents and agents may have conflicting preferences over allocations. Examples of the resource allocation problem include electronic commerce (where resources are commodities equipped with prices), the grid (where resources are computational entities equipped with computational power), and scheduling and timetabling (where resources may be tasks with costs). The focus in this thesis is distributed decision-making amongst agents, whereby agents actively participate in computing re-allocations, starting from initial allocations which may or may not fulfil their goals. A re-allocation is arrived at by means of local negotiation steps wherein resources change hands between the agents involved in the negotiations. The negotiation method of choice in this thesis is argumentation-based negotiation supported by assumption-based argumentation. This method allows agents to work towards their goals despite incomplete information regarding the goals of and resources allocated to other agents, to share knowledge, thereby eliminating unknowns, and to resolve conflicts within themselves and between one another which may arise because of inconsistent information. Solutions generated by a resource allocation mechanism may be ranked according to how they affect the individual welfare of the agents as well as the overall social welfare of the agent society, according to different notions of social welfare borrowed from economics. The argumentation-based negotiation mechanism we propose guarantees, for the problem domain of interest in this thesis, that negotiations between agents always terminate converging to a solution. Moreover, the mechanism guarantees that solutions reached optimise the welfare of the individual agents as well as the agent society as a whole according to Pareto optimal and utilitarian notions of social welfare

Using Enthymemes in an Inquiry Dialogue System

A common assumption for logic-based argumentation is that an argument is a pair 〈Φ, α 〉 where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i.e. arguments presented by humans) usually do not have enough explicitly presented premises for the entailment of the claim (i.e. they are enthymemes). This is because there is some common knowledge that can be assumed by a proponent of an argument and the recipient of it. This allows the proponent of an argument to encode an argument into a real argument by ignoring the common knowledge, and it allows a recipient of a real argument to decode it into the intended argument by drawing on the common knowledge. If both the proponent and recipient use the same common knowledge, then this process is straightforward. Unfortunately, this is not always the case, and this raises interesting issues for dialogue systems in which the recipient has to cope with the disparities between the different views on what constitutes common knowledge. Here we investigate the use of enthymemes in inquiry dialogues. For this, we propose a generative inquiry dialogue system and show how, in this dialogue system, enthymemes can be managed by the agents involved, and how common knowledge can evolve through dialogue