Different approaches analyze the strength of a natural language argument in different ways. This paper contrasts the dialectical, structural, probabilistic (or Bayesian), computational, and empirical approaches by exemplarily applying them to a single argumentative text (Epicureans on Squandering Life; Aikin & Talisse, 2019). Rather than pitching these approaches against one another, our main goal is to show the room for fruitful interaction. Our focus is on a dialectical analysis of the squandering argument as an argumentative response that voids an interlocutor’s right to assertion. This analysis addresses the pragmatic dimensions of arguing and implies an argument structure that is consistent with empirical evidence of perceived argument strength. Results show that the squandering argument can be evaluated as a (non-fallacious) ad hominem argument, which however is not necessarily stronger than possible arguments attacking it.

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.

This paper considers the probative burdens of proposing action or policy options in deliberation dialogues. Do proposers bear a burden of proof? Building on pioneering work by Douglas Walton (2010), and following on a growing literature within computer science, the prevailing answer seems to be “No.” Instead, only recommenders—agents who put forward an option as the one to be taken—bear a burden of proof. Against this view, we contend that proposers have burdens of proof with respect to their proposals. Specifically, we argue that, while recommenders that Φ bear a burden of proof to show that □Φ (We should / ought to / must Φ), proposers that Φ have a burden of proof to show that ◇Φ (We may / can Φ). A burden of proposing may be defined as , which reads: Those who propose that we might Φ are obliged, if called upon, to show that Φ is possible in any of four ways which we call worldly, deontic, instrumental, and practical. So understood, burdens of proposing satisfy the standard formal definition of burden of proof.

This paper considers the probative burdens of proposing action or policy options in deliberation dialogues. Do proposers bear a burden of proof? Building on pioneering work by Douglas Walton (2010), and following on a growing literature within computer science, the prevailing answer seems to be "No." Instead, only recommenders-agents who put forward an option as the one to be taken-bear a burden of proof. Against this view, we contend that proposers have burdens of proof with respect to their proposals. Specifically, we argue that, while recommenders that Phi bear a burden of proof to show that square Phi (We should / ought to / must Phi), proposers that Phi have a burden of proof to show that lozenge Phi (We may / can Phi). A burden of proposing may be defined as , which reads: Those who propose that we might Phi are obliged, if called upon, to show that Phi is possible in any of four ways which we call worldly, deontic, instrumental, and practical. So understood, burdens of proposing satisfy the standard formal definition of burden of proof.

This paper reports the design and comparison of three visualizations to represent the structure and content within arguments. Arguments are artifacts of reasoning widely used across domains such as education, policy making, and science. An argument is made up of sequences of statements (premises) which can support or contradict each other, individually or in groups through Boolean operators. Understanding the resulting hierarchical structure of arguments while being able to read the arguments' text poses problems related to overview, detail, and navigation. Based on interviews with argument analysts we iteratively designed three techniques, each using combinations of tree visualizations (sunburst, icicle), content display (in‐situ, tooltip) and interactive navigation. Structured discussions with the analysts show benefits of each these techniques; for example, sunburst being good in presenting overview but showing arguments in‐situ is better than pop‐ups. A controlleduser study with 21 participants and three tasks shows complementary evidence suggesting that a sunburst with pop‐up for the content is the best trade‐off solution. Our results can inform visualizations within existing argument visualization tools and increase the visibility of ‘novel‐and‐effective’ visualizations in the argument visualization community.

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.

This paper introduces SADFace, a simple argument description format, and ArgDB, a datastore for managing datasets of SADface documents, in the wider context of a nascent effort to develop an Open Argumentation platform.

We sketch the framework for a theoretical and applied system that we are developing which uses argumentation schemes and dialogue games to support dialectical interaction between people and machine learning systems. The goal is to support the automated justification and explanation of decisions made by AI systems, through a natural, human-oriented interface, in response to contemporary societal concerns about the impact of AI decisions upon individuals.

Dark patterns are interactive design patterns that influence technology users through deception or trickery, and which represent unethical applications of persuasive technology. However, our ability to identify dark patterns is limited, creating a situation where it is difficult to manage abuses of persuasive psychology, because it is difficult to even identify them. Although there are numerous practitioner taxonomies of dark patterns, there is no scientifically-based taxonomy available. This workshop provides an introduction to dark patterns and an overview of the psychological mechanisms that drive them. Through participatory exercises, participants will help to identify the theoretical underpinnings that drive dark patterns, and contribute to the development of a taxonomy of dark patterns, based on consensus within the scientific community. In the workshops, we will form working teams who will review the dark pattern taxonomy, looking for alternative theoretical explanations. Each working team will participate in a group sorting exercise, designed to inform the development of a theoreticallyframed taxonomy of dark patterns. All outputs of the workshop will be captured, and used to advance this study towards validation of the taxonomy. After the workshops, the authors of this paper will incorporate all the advancements into the next stage of the research, which will feed into a subsequent paper on a taxonomy of dark patterns, addressing the identified research questions.

Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?

Argumentative dialogue systems can provide human-oriented interaction mechanisms between people and artificially intelligent machines. Questions remain about how normative systems of argument and dialogue fare when exposed to real-world arguers. It’s often assumed that the truth should always be told, but even when achievable, can be counterproductive. We shed light on some gray areas concerning truth telling, or lack thereof, in relation to human dialogical interaction with AI systems.

Simulations of real world scenarios often require considerably large numbers of agents. With increasing level of detail and resolution in the underlying models machine limitations both in the aspect of memory and computing power are reached. Even more when additional features like reasoning mechanisms of semantic technologies are used as in the AGADE framework where we have extended the principal BDI paradigm with an interface to OWL ontologies. We have observed that the extensive use of ontologies results in high memory consumption due to the large number of String objects used in the reasoning process and caching mechanisms of the OWL API. We address this issue by running simulations in a highly distributed environment. In this paper we demonstrate how we enabled AGADE to be run in such an environment and the necessary architectural modifications. Furthermore, we discuss the potential size of simulations that can be run in such a setting.

Arguments are structures of premises and conclusions that underpin rational reasoning processes. Within complex knowledge domains, especially if they are contentious, argument structures can become large and complex. Visualization tools have been developed that support argument analysts and help them to work with arguments. Until recently, arguments were manually analyzed from natural language text, or constructed from scratch, but new communication modes mean that increasing amounts of debate and the arguments therein can be captured digitally. Furthermore, new tools and techniques for argument mining are beginning to automate the process of extracting argument structure from natural language; leading to much larger argument datasets that present problems for the current generation of argument visualization tools. Additionally, individual argument analysts have different foci which can lead to increased complexity within datasets, and additional facets that argument visualizations should account for but do not. We propose a tool for interacting with argument corpora that enable users to explore and understand the reasoning structure of large-scale arguments. The tool will support a range of interactivity techniques and will help users to explore and analyse large-scale arguments, to more rapidly comprehend complex new problem domains.

Within business games there is a need to provide realistic feedback for decisions made, if such business games are to continue to remain relevant in increasingly complex business environments. We address this problem by using software agents to simulate individuals and to model their actions in response to business decisions. In our initial studies we have used software agents to simulate consumers who make buying decisions based on their private preferences and those prevalent within their social network. This approach can be applied to search for behavioural patterns in social structures and to verify predicted values based on a priori theoretical considerations. Individual behaviour can be modelled for each agent and its effects within the marketplace can be examined by running simulations. Our simulations are founded upon the BDI software model (belief-desire-intention) combined with ontologies to make world knowledge available to the agents which can then determine their actions in accordance with this knowledge. We demonstrate how ontologies can be integrated into the BDI concept utilising the Jadex agent framework. Our examples are based upon the simulation of market mechanisms within the context of different industries. We use a framework, developed previously, known as AGADE within which each agent evolves its knowledge using an ontology maintained during the simulation. This generic approach allows the simulation of various consumer scenarios which can be modelled by creating appropriate ontologies.

ALIAS is a Python library for constructing, manipulating, storing, visualising, and converting argumentation structues. It is available with full source code under a copyleft license and aims to become a Swiss Army Knife for working with arguments in a variety of end-user, researcher, pedagogical, and developer contexts.