Virtual humans need to be persuasive in order to promote behaviour change in human users. While several studies have focused on understanding the numerous aspects that influence the degree of persuasion, most of them are limited to dyadic interactions. In this paper, we present an evaluation study focused on understanding the effects of multiple agents on user's persuasion. Along with gender and status (authoritative & peer), we also look at type of focus employed by the agent i.e., user-directed where the agent aims to persuade by addressing the user directly and vicarious where the agent aims to persuade the user, who is an observer, indirectly by engaging another agent in the discussion. Participants were randomly assigned to one of the 12 conditions and presented with a persuasive message by one or several virtual agents. A questionnaire was used to measure perceived interpersonal attitude, credibility and persuasion. Results indicate that credibility positively affects persuasion. In general, multiple agent setting, irrespective of the focus, was more persuasive than single agent setting. Although, participants favored user-directed setting and reported it to be persuasive and had an increased level of trust in the agents, the actual change in persuasion score reflects that vicarious setting was the most effective in inducing behaviour change. In addition to this, the study also revealed that authoritative agents were the most persuasive.

To adequately model mathematical arguments the analyst must be able to represent the mathematical objects under discussion and the relationships between them, as well as inferences drawn about these objects and relationships as the discourse unfolds. We introduce a framework with these properties, which has been used to analyse mathematical dialogues and expository texts. The framework can recover salient elements of discourse at, and within, the sentence level, as well as the way mathematical content connects to form larger argumentative structures. We show how the framework might be used to support computational reasoning, and argue that it provides a more natural way to examine the process of proving theorems than do Lamport's structured proofs.

'17) was hosted at the Georgia Institute This was the third time the conference had been hosted in North America (Mexico City, ICCC'11; Park City, ICCC'15), and the Georgia Institute of Technology and local hosts provided extremely comfortable accommodation for everyone, furthering the traditional friendly and welcoming atmosphere of the conference. Thirty-four full papers were presented in a single track over three and a half days, as oral presentations, or posters and short talks, depending on the nature of the contribution. The papers were grouped by theme. A foundations session opened the conference with talks on application domains in CC, building a CC system, and teaching CC. A language session followed, looking at linguistic creativity in narrative and poetry.

Building on a survey of previous theories of serendipity and creativity, we advance a sequential model of serendipitous occurrences. We distinguish between serendipity as a service and serendipity in the system itself, clarify the role of invention and discovery, and provide a measure for the serendipity potential of a system. While a system can arguably not be guaranteed to be serendipitous, it can have a high potential for serendipity. Practitioners can use these theoretical tools to evaluate a computational system's potential for unexpected behaviour that may have a beneficial outcome. In addition to a qualitative features of serendipity potential, the model also includes quantitative ratings that can guide development work. We show how the model is used in three case studies of existing and hypothetical systems, in the context of evolutionary computing, automated programming, and (next-generation) recommender systems. From this analysis, we extract recommendations for practitioners working with computational serendipity, and outline future directions for research.

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.

Refinement is a powerful mechanism for mastering the complexities that arise when formally modelling systems. Refinement also brings with it additional proof obligations -- requiring a developer to discover properties relating to their design decisions. With the goal of reducing this burden, we have investigated how a general purpose theory formation tool, HR, can be used to automate the discovery of such properties within the context of Event-B. Here we develop a heuristic approach to the automatic discovery of invariants and report upon a series of experiments that we undertook in order to evaluate our approach. The set of heuristics developed provides systematic guidance in tailoring HR for a given Event-B development. These heuristics are based upon proof-failure analysis, and have given rise to some promising results.