ArguBlogging:an application for the Argument Web

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

On the role of dialogue models in the age of large language models.

We argue that Machine learning, in particular the currently prevalent generation of Large Language Models (LLMs), can work constructively with existing normative models of dialogue as exemplified by dialogue games, specifically their computational applications within, for example, inter-agent communication and automated dialogue management. Furthermore we argue that this relationship is bi-directional, that some uses of dialogue games benefit from increased functionality due to the specific capabilities of LLMs, whilst LLMs benefit from externalised models of, variously, problematic, normative, or idealised behaviour. Machine Learning (ML) approaches, especially LLMs , appear to be making great advances against long-standing Artificial Intelligence challenges. In particular, LLMs are increasingly achieving successes in areas both adjacent to, and overlapping with, those of interest to the Computational Models of Natural Argument community. A prevalent opinion, not without some basis, within the ML research community is that many, if not all, AI challenges, will eventually be solved by ML models of increasing power and utility, negating the need for alternative or traditional approaches. An exemplar of this position, is the study of distinct models of dialogue for inter-agent communication when LLM based chatbots are increasingly able to surpass their performance in specific contexts. The trajectory of increased LLM capabilities suggests no reason that this trend will not continue, at least for some time. However, it is not the case that only the one, or the other approach, is necessary. Despite a tendency for LLMs to feature creep, and to appear to subsume additional areas of study, there are very good reasons to consider three modes of study of dialogue. Firstly, LLMs as their own individual field within ML, secondly, dialogue both in terms of actual human behaviour, which can exhibit wide quality standards, but also in terms of normative and idealised models, and thirdly, the fertile area in which the two overlap and can operate collaboratively. It is this third aspect with which this paper is concerned, for the first will occur anyway as researchers seek to map out the boundaries of what LLMs, as AI models, can actually achieve, and the second will continue, because the study of how people interact naturally through argument and dialogue will remain both fascinating and of objective value regardless of advances made in LLMs. However, where LLMs, Dialogue Models, and, for completion, people, come together, there is fertile ground for the development of principled models of interaction that are well-founded, well-regulated, and supportive of mixed-initiative interactions between humans and intelligent software agents

Towards a declarative approach to constructing dialogue games.

In this paper we sketch a new approach to the development of dialogue games that builds upon the knowledge gained from several decades of dialogue game research across a variety of communities and which leverages the capabilities of the Dialogue Game Description Language as a means to describe the constituent parts of dialogue games. Our ultimate aim is to produce a method for rapidly describing and implementing games that conform to the designer's needs by declaring what is required and then automatically constructing the game from components, called 'fragments', that are distilled from existing dialogue games

Reconsidering RepStat rules in dialectic games.

Prohibition of repeated statements has benefits for the tractability and predictability of dialogues carried out by machines, but doesn't match the real world behaviour of people. This gap between human and machine behaviour leads to problems when formal dialectical systems are applied in conversational AI contexts. However, the problem of handling statement repetition gives insight into wider issues that stem partly from the historical focus on formal dialectics to the near exclusion of descriptive dialectics. In this paper we consider the problem of balancing the needs of machines versus those of human participants through the consideration of both descriptive and formal dialectics integrated within a single overarching dialectical system. We describe how this approach can be supported through minimal extension of the Dialogue Game Description Language

Discretisation for odd quadratic twists

The discretisation problem for even quadratic twists is almost understood, with the main question now being how the arithmetic Delaunay heuristic interacts with the analytic random matrix theory prediction. The situation for odd quadratic twists is much more mysterious, as the height of a point enters the picture, which does not necessarily take integral values (as does the order of the Shafarevich-Tate group). We discuss a couple of models and present data on this question.Comment: To appear in the Proceedings of the INI Workshop on Random Matrix Theory and Elliptic Curve

Ethical challenges in argumentation and dialogue in a healthcare context.

As the average age of the population increases, so too do the number of people living with chronic illnesses. With limited resources available, the development of dialogue-based e-health systems that provide justified general health advice offers a cost-effective solution to the management of chronic conditions. It is however imperative that such systems are responsible in their approach. We present in this paper two main challenges for the deployment of e-health systems, that have a particular relevance to dialogue and argumentation: collecting and handling health data, and trust. For both challenges, we look at specific issues therein, outlining their importance in general, and describing their relevance to dialogue and argumentation. Finally, we go on to propose six recommendations for handling these issues, towards addressing the main challenges themselves, that act both as general advice for dialogue and argumentation research in the e-health domain, and as a foundation for future work on this topic