Simon Wells

A domain specific language for describing diverse systems of dialogue

Abstract This paper introduces the Dialogue Game Description Language (DGDL), a domain specific language for describing dialectical games. Communication is an important topic within agent research and is a fundamental factor in the development of robust and efficient multiagent systems. Similarly, argumentation has been recognised as a key component of an agentʼs ability to make decisions using complex, dynamic, uncertain, and incomplete knowledge. Dialectical games, a type of multi-player argumentative dialogue game, provide a mechanism for communication which incorporates argumentative behaviours. However there are very few tools for working with these games and little agreement over how they should be described, shared, and reused. The DGDL provides a grammar for determining whether a game description is syntactically correct and thus provides a foundation for new tools to support the future development and wider exploitation of dialectical games.

Designing motivational features for sustainable urban mobility

This paper describes the User-Centred Design process followed within the SUPERHUB project in order to investigate requirements, elicit user feedback and refine motivational features of a mobile app fostering the adoption of sustainable behaviors for urban mobility. We report the main lessons learnt from focus groups, participatory design sessions, and low-mid fidelity prototyping of the motivational features designed. These lessons are currently informing our implementation work in SUPERHUB and could be of interest for designers in the eco-sustainability field.

Towards an applied gamification model for tracking, managing, & encouraging sustainable travel behaviours.

In this paper we introduce a gamification model for encouraging sustainable multi-modal urban travel in modern European cities. Our aim is to provide a mechanism that encourages users to reflect on their current travel behaviours and to engage in more environmentally friendly activities that lead to the formation of sus- tainable, long-term travel behaviours. To achieve this our users track their own be- haviours, set goals, manage their progress towards those goals, and respond to chal- lenges. Our approach uses a point accumulation and level achievement metaphor to abstract from the underlying specifics of individual behaviours and goals to allow an extensible and flexible platform for behaviour management. We present our model within the context of the SUPERHUB project and platform.

Supporting Argumentation Schemes in Argumentative Dialogue Games

Abstract This paper reports preliminary work into the exploitation of argumentation schemes within dialogue games. We identify a property of dialogue games that we call “scheme awareness” that captures the relationship between dialogue game systems and argumentation schemes. Scheme awareness is used to examine the ways in which existing dialogue games utilise argumentation schemes and consequently the degree with which a dialogue game can be used to construct argument structures. The aim is to develop a set of guidelines for dialogue game design, which feed into a set of Dialogue Game Description Language (DGDL) extensions in turn enabling dialogue games to better exploit argumentation schemes.

Towards using Segmentation-based Techniques to Personalize Mobility Behavior Interventions

This paper describes our initial work towards a segmentation-based approach to personalized digital behavior change interventions in the domain of sustainable, multi-modal urban transport. Segmentation is a key concept in market research, and within the transport domain, Anable has argued that there are segments of travelers that are relatively homogenous in terms of their mobility attitudes and behaviors. We describe an approach aimed at tailoring behavior change notifications by using segmentation-based techniques for user profiling. We report results from a Mechanical Turk study in which we obtained a crowd-sourced categorization of motivational messages. This is a first step towards understanding how to better deliver persuasive messages to relevant users profiles and situational contexts in the urban mobility domain. We conclude by discussing future steps of our work that should inform the deployment of persuasion profiling techniques to achieve sustainable mobility goals.

Large-scale argument visualization (LSAV)

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.

Ontology Based Business Simulations

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.

Demo Paper: AGADE

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.

Automatically Detecting Fallacies in System Safety Arguments

Safety cases play a significant role in the development of safety-critical systems. The key components in a safety case are safety arguments, that are designated to demonstrate that the system is acceptably safe. Inappropriate reasoning with safety arguments could undermine a system’s safety claims which in turn contribute to safety-related failures of the system. Currently, safety argument reviews are conducted manually, require expensive expertise and are often labour intensive. It would therefore be desirable if software can be employed to help with the detection of flaws in the arguments. A prerequisite for this approach is the need for a formal representation of safety arguments. This paper proposes a predicate logic based representation of safety arguments and a method to detect argument fallacies. It is anticipated that the work contributes to the field of the safety case development as well as to the area of computational fallacies.