Using Out-of-Character Reasoning to Combine Storytelling and Education in a Serious Game

To reconcile storytelling and educational meta-goals in the context of a serious game, we propose to make use of out-of-character reasoning in virtual agents. We will implement these agents in a serious game of our design, which will focus on social interaction in conflict scenarios with the meta-goal of improving social awareness of users. The agents will use out-of-character reasoning to manage conflicts by assuming different in-character personalities or by planning to take specific actions based on interaction with the users. In-character reasoning is responsible for the storytelling concerns of character believability and consistency. These are not endangered by out-of-character reasoning, as it takes in-character information into account when making decisions

Meta-techniques for a social awareness learning game

In live action role play, so-called meta-techniques are used, in which meta-information (information outside of the enacted world setting, such as learning goals) is taken into account and used during play. We show how we intend to include two such meta-techniques in LOITER, a serious game about social interaction. In this game, players can interact with virtual characters with the ultimate goal of improving their own social awareness. We have constructed sets of learning goals around the interpersonal circumplex, a model for social interaction also used in the training curriculum of the police academy. The meta-techniques we are developing can assist players in attaining these learning goals by providing both feedback and insight into what is at play in characters' minds. The first meta-technique is that of act breaks: intermissions between acts of a live action role play during which players discuss the previous and following acts. In LOITER, consecutive acts have increasing levels of interaction complexity, corresponding to our sets of learning goals. In the act breaks, the trainee and the virtual character exchange information about their motivations for certain actions and what the effects of these actions were. This helps the trainees analyse the situation and their own actions. Secondly, feedback on the actions of trainees can be given in a less intrusive way during the acts themselves. We show, based on another meta-technique, how virtual characters can show 'thought bubbles' which give insight in their personal thoughts and thus help the trainees determine the reasons behind their behaviour

AGENT: Awareness Game Environment for Natural Training

We propose AGENT, the Awareness Game Environment for Natural Training, as a virtual environment in which serious games can be enacted. AGENT combines research on interactive storytelling, game design, turn-taking and social signal processing with a multi-modal UI in a modular fashion. Current work in progress will deliver a rst demonstrable prototype within 2013

Taking things at face value: how stance informs politeness of virtual agents

We propose a combination of theories about interpersonal stances and politeness to inform virtual agent behaviour in a serious game, aimed at social interaction training in the domain of law enforcement. A pilot study we conducted showed promising results based on which we will refine our model

Hide and sneak: story generation with characters that perceive and assume

We describe the design of a perception system for the characters in the Virtual Storyteller (VST), a character-centric story generation system. Previously, these characters were omniscient; stories involving sneaking and deception could not be generated. To remedy this, we limited the characters' visual perception using simple rules. We enabled the characters to make assumptions about the story world, so they can plan toward goals in spite of incomplete knowledge. Using the distinction between the character and actor roles of agents in the VST, we can use the assumptions to steer the story plot

Beyond simulations : serious games for training interpersonal skills in law enforcement

Serious games can be used to improve people's social awareness by letting them experience difficult social situations and learn from these experiences. However, we assert that, when moving beyond the strict realism that social simulations offer, techniques from role play may be used that offer more possibilities for feedback and reflection. We discuss the design of two such serious games for interpersonal skills training in the domain of law enforcement. These games feature intelligent virtual agents with which trainees have to interact across different scenarios to improve their social awareness. By interacting with the virtual agents, trainees experience how their behaviour influences the course of the intervention and its outcomes. We discuss how we intend to improve the learning experience in these serious games by including meta-techniques from role play. We close by describing the current and future implementations of our serious games

R3D3 in the Wild: Using A Robot for Turn Management in Multi-Party Interaction with a Virtual Human

R3D3 is a combination of a virtual human with a non-speaking robot capable of head gestures and emotive gaze behaviour. We use the robot to implement various turn management functions for use in multi-party interaction with R3D3, and present the results of a field study investigating their effects on interactions with groups of children

Adapting the complexity level of a serious game to the proficiency of players

As games are continuously assessing the player, this assessment can be used to adapt the complexity of a game to the proficiency of the player in real time. We performed an experiment to examine the role of dynamic adaptation. In one condition, participants played a version of our serious game for triage training that automatically adapted the complexity level of the presented cases to how well the participant scored previously. Participants in the control condition played a version of the game with no adaptation. The adapted version was significantly more efficient and resulted in higher learning gains per instructional case, but did not lead to a difference in engagement. Adapting games to the proficiency of the player could make serious games more efficient learning tools