EQRbot: A chatbot delivering EQR argument-based explanations

Data availability statement: The provided link: https://github.com/FCast07/EQRbot refers to the GitHub repository that stores the chatbot programming code.Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents.This research was partially funded by the UK Engineering & Physical Sciences Research Council (EPSRC) under Grant #EP/P010105/1

A clinical, radiological and histopathological review of 74 ossifying fibromas

Background Ossifying fibroma (OF) is a fibro-osseous lesion of the jaws and craniofacial bones. Accurate diagnosis can be challenging due to significant overlap of clinicopathological features. This study aimed to evaluate the clinical, radiological and histological features that can aid in diagnosis and identify characteristics that allow categorisation into the three subtypes: juvenile trabecular, psammomatoid and cemento-ossifying OF. Methods A total of 74 cases of OF were systematically reviewed for their principle features. Of these, 46 cases were evaluated for their radiographic features including size, location and relationship to the teeth. Histological assessment and stereological point counting were performed in 69 cases to assess the pattern, type and proportion of calcification, the nature of the stroma, the border of the lesion and the presence of secondary changes. Fisher’s exact test and Chi-squared tests were used to determine associations between clinicopathological parameters and maxillary, mandibular, odontogenic, non-odontogenic and psammomatoid or trabecular lesions. Results OF showed a female predilection (F: M; 2:1) and a slight bimodal age distribution with peaks in the second (23%) and fourth decades (27%) (Mean age: 32.4 years). 83% of cases presented as an intra-oral swelling, with the mandible being the most common site (73%). Histologically, a range of morphological patterns were seen, with 50% of cases showing mixed trabecular and psammomatoid features. However, there were no significant differences between the variants of OF in terms of age, gender or histological features. Conclusion Histological features of OF cannot be used to differentiate between the subtypes

A formalisation and prototype implementation of argumentation for statistical model selection

© 2019 – IOS Press and the authors. The task of data collection is becoming routine in many disciplines and this results in increased availability of data. This routinely collected data provides a valuable opportunity for analysis with a view to support evidence based decision making. In order to confidently leverage the data in support of decision making the most appropriate statistical method needs to be selected, and this can be difficult for an end user not trained in statistics. This paper outlines an application of argumentation to support the analysis of clinical data, that uses Extended Argumentation Frameworks in order to reason with the meta-level arguments derived from preference contexts relevant to the data and the analysis objective of the end user. We outline a formalisation of the argument scheme for statistical model selection, its critical questions and the structure of the knowledge base required to support the instantiation of the arguments and meta-level arguments through the use of Z notation. This paper also describes the prototype implementation of argumentation for statistical model selection based on the Z specification outlined herein.CONSULT EPSRC grant no. EP-P010105-1

On the Complexity of Determining Defeat Relations Consistent with Abstract Argumentation Semantics

Presented at Computational Models of Argument Proceedings of COMMA 2022 ((9th International Conference on Computational Models of Argument COMMA 2022, Cardiff, UK, 14-16 September, 2022) Available at https://ebooks.iospress.nl/ISBN/978-1-64368-306-5Copyright 2022 The authors and IOS Press. Typically in abstract argumentation, one starts with arguments and a defeat relation, and applies some semantics in order to determine the acceptability status of the arguments. We consider the converse case where we have knowledge of the acceptability status of arguments and want to identify a defeat relation that is consistent with the known acceptability data – the σ-consistency problem. Focusing on complete semantics as underpinning the majority of the major semantic types, we show that the complexity of determining a defeat relation that is consistent with some set of acceptability data is highly dependent on how the data is labelled. The extension-based 2-valued σ-consistency problem for complete semantics is revealed as NP-complete, whereas the labelling-based 3-valued σ-consistency problem is solvable within polynomial time. We then present an informal discussion on application to grounded, stable, and preferred semantics

Computational Argumentation-based Chatbots: a Survey

The article archived on this institutional repository is a preprint. It has not been certified by peer review.Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models

EQRbot: A chatbot delivering EQR argument-based explanations

Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents

Reading, Trauma and Literary Caregiving 1914-1918: Helen Mary Gaskell and the War Library

This article is about the relationship between reading, trauma and responsive literary caregiving in Britain during the First World War. Its analysis of two little-known documents describing the history of the War Library, begun by Helen Mary Gaskell in 1914, exposes a gap in the scholarship of war-time reading; generates a new narrative of "how," "when," and "why" books went to war; and foregrounds gender in its analysis of the historiography. The Library of Congress's T. W. Koch discovered Gaskell's ground-breaking work in 1917 and reported its successes to the American Library Association. The British Times also covered Gaskell's library, yet researchers working on reading during the war have routinely neglected her distinct model and method, skewing the research base on war-time reading and its association with trauma and caregiving. In the article's second half, a literary case study of a popular war novel demonstrates the extent of the "bitter cry for books." The success of Gaskell's intervention is examined alongside H. G. Wells's representation of textual healing. Reading is shown to offer sick, traumatized and recovering combatants emotional and psychological caregiving in ways that she could not always have predicted and that are not visible in the literary/historical record

FACS: A geospatial agent-based simulator for analyzing COVID-19 spread and public health measures on local regions

This is a preprint of an article to be published by Taylor & Francis in Journal of Simulation on [date of publication], available online: https://www.tandfonline.com/[Article DOI].”European Union Horizon 2020 research and innovation programme under grant agreement No 824115 and 80092

Towards an Argumentation System for Supporting Patients in Self-Managing their Chronic Conditions

EPSR