Approximate Solutions to Abstract Argumentation Problems Using Graph Neural Networks

This thesis explores a new approach to approximating decision problems in abstract argumentation using Graph Convolutional Networks (GCN). It demonstrates that such an approach can reach well-balanced accuracy levels above 90 \% across a range of different decision problems, argumentation semantics, and benchmarks. This thesis develops a new Deep Neural Network (DNN) architecture adapted from the classic GCN that better addresses the specific issues found in abstract argumentation. Likewise, it develops a training approach that produces superior results for abstract argumentation data sets by introducing structured randomness and dynamic adaptation to the training data. Then, the thesis systematically applies this architecture to a large argumentation dataset across the main argumentation semantics used in the biannual ICCMA competition. It evaluates the performance of the model in a variety of different settings and across benchmarks, size bands, and model variants. The main models show good performance in the majority of cases, although there is some variation. Having created the core model, the thesis goes on to explore additional extensions of the core work. This first focuses on combining the approximate approach with exact approaches using a deterministic algorithm and a SAT solver, showing an improvement by solving six additional hard instances relative to existing solvers. Second, we explore a visualisation approach that can give new insights into argumentation graphs by applying a dimensionality reduction technique to weights from the trained GCN models, showing new insights in explaining benchmark performance. Finally, we explore using the same basic architecture to address another problem that can be structured using abstract argumentation. In this case, we apply the approach to the prediction of misinformation in tweets and achieve good performance on a key dataset

CIRCULATING KNOWLEDGE OR SUPERSTITION?: THE DUTCH DEBATE ON DIVINATION

This article describes the Dutch reception of an international controversy about the divining rod. gives us a beautiful picture of the ways in which natural philosophy was practiced and disseminated in the Low Countries at the turn of the seventeenth century. It offers us an idea of the scientific demonstrations going on in Dutch bourgeois domestic settings, the personal contacts by which scientific claims were transferred, as well as the ways in which controversies were initiated and perpetuated. This sometimes intense and venomous controversy between advocates and opponents of the divining rod developed in journal publications, books and pamphlets, enrolling the local doctors, literati and savants, but also drawing in intellectuals of international stature. On a theoretical level, I will take the notion of 'circulation of knowledge' seriously. Asking whether 'knowledge' - both skills and theoretical knowledge - is something that can be circulated has led me to distinguish three crucial levels in the circulation of knowledge

Foundations of implementations for formal argumentation

We survey the current state of the art of general techniques, as well as specific software systems for solving tasks in abstract argumentation frameworks, structured argumentation frameworks, and approaches for visualizing and analysing argumentation. Furthermore, we discuss challenges and promising techniques such as parallel processing and approximation approaches. Finally, we address the issue of evaluating software systems empirically with links to the International Competition on Computational Models of Argumentation

Telegraphy is an occult art: Cromwell Fleetwood Varley and the diffusion of electricity to the other world

© Cambridge University Press 1999. Paper condensed from chapters of the author's doctoral dissertation. Illustrations 1,2,4 & 5 reproduced by permission of the British Library © British Library Board. All Rights Reserved (4367.150000 DSC; 4367.215500 DSC; Hendon; Hendon). Illustration 3 reproduced by permission of the College of Psychic Studies. All quotes from archival sources reproduced with the permission of the rightsholders. References for the BT archives have been recatalogued: POST 81/45 is now TGA/1/10/1; POST 81/20 is now TGA/1/7/1; POST 81/41 is now TGE/1/13; POST 81/19 is now TGA/2/1; POST 81/27 is now TGE/1/11.In May 1862 Desmond G. Fitzgerald, the editor of the Electrician, lamented that "telegraphy has been until lately an art occult even to many of the votaries of electrical science. Submarine telegraphy, initiated by a bold and tentative process – the laying of the Dover cable in the year 1850 – opened out a vast field of opportunity both to merit and competency, and to unscrupulous determination. For the purposes of the latter, the field was to be kept close [sic], and science, which can alone be secured by merit, more or less ignored". To Fitzgerald, the ‘occult’ status of the telegraph looked set to continue, with recent reports of scientific counterfeits, unscrupulous electricians and financially motivated saboteurs involved in the telegraphic art. Nevertheless, Fitzgerald reassured his readers that the confidence of ‘those who act for the public’ had been restored by earnest electricians, whose ‘moral cause’ would ultimately be felt and who ‘may be safely trusted even in matters where there is an option between a private interest and a public benefit’. As a prominent crusader for the telegraph, Fitzgerald voiced the concerns of many electricians seeking public confidence and investment in their trade in the wake of the failed submarine telegraphs of the 1850s. The spread of proper knowledge about the telegraph would hinge on securing an adequate supply of backers and the construction of telegraphy as a truly moral cause – an art cleansed of fraudsters, ignoramuses and dogmatists.British Academ

Permission to believe: descriptive and prescriptive beliefs in the Clifford/James debate

William Clifford's ‘The Ethics of Belief' proposes an ‘evidence principle': …it is wrong always, everywhere, and for anyone, to believe anything upon insufficient evidence (1877, 1879:186). Its universal, absolutist language seems to hide something fundamentally correct. We first argue for excluding prescriptive beliefs, and then consider further apparent counter-examples, culminating in more restricted, qualified wording: If anything is morally wrong, then it is morally wrong within the category of descriptive belief to believe anything knowingly or irresponsibly on insufficient evidence in the absence of any conflicting and overriding moral imperative except when the unjustified believing is outside the believer's voluntary control. We test this against William James's counter-claim for qualified legitimate overbelief (‘The Will To Believe', 1896, 2000), and suggest additional benefits of adopting an evidence principle in relation to the structured combinations of descriptive and prescriptive components common to religious belief. In search of criteria for ‘sufficient' and ‘insufficient' evidence we then consider an ‘enriched' Bayesianism within normative decision theory, which helps explain good doxastic practice under risk. ‘Lottery paradox' cases however undermine the idea of an evidence threshold: we would say we justifiably believe one hypothesis while saying another, at the same credence level, is only very probably true. We consider approaches to ‘pragmatic encroachment', suggesting a parallel between ‘practical interest' and the ‘personal utility' denominating the stakes of the imaginary gambles which Bayesian credences can be illustrated as. But personal utility seems inappropriately agent-relative for a moral principle. We return to Clifford's conception of our shared responsibilities to our shared epistemic asset. This ‘practical interest we ought to have' offers an explanation for our duty, as members of an epistemic community, to get and evaluate evidence; and for the ‘utility' stakes of Bayesian imaginary gambles. Helped by Edward Craig's (1990, 1999) ‘state-of-nature' theory of knowledge it provides a minimum threshold to avoid insufficient evidence and suggests an aspirational criterion of sufficient evidence: Wherever possible, a level of evidence sufficient to support the level of justification required to be a good informant, whatever the particular circumstances of the inquirer

Argumentation in biology : exploration and analysis through a gene expression use case

Argumentation theory conceptualises the human practice of debating. Implemented as computational argumentation it enables a computer to perform a virtual debate. Using existing knowledge from research into argumentation theory, this thesis investigates the potential of computational argumentation within biology. As a form of non-monotonic reasoning, argumentation can be used to tackle inconsistent and incomplete information - two common problems for the users of biological data. Exploration of argumentation shall be conducted by examining these issues within one biological subdomain: in situ gene expression information for the developmental mouse. Due to the complex and often contradictory nature of biology, occasionally it is not apparent whether or not a particular gene is involved in the development of a particular tissue. Expert biological knowledge is recorded, and used to generate arguments relating to this matter. These arguments are presented to the user in order to help him/her decide whether or not the gene is expressed. In order to do this, the notion of argumentation schemes has been borrowed from philosophy, and combined with ideas and technologies from arti cial intelligence. The resulting conceptualisation is implemented and evaluated in order to understand the issues related to applying computational argumentation within biology. Ultimately, this work concludes with a discussion of Argudas - a real world tool developed for the biological community, and based on the knowledge gained during this work

Predictive Models and Abstract Argumentation: the case of High-Complexity Semantics

In this paper we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features— i.e., values that summarise a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way