Marieke M.M. Peeters

The design and effect of automated directions during scenario-based training

During scenario-based training, the scenario is dynamically adapted in real time to control the storyline and increase its effectiveness. A team of experienced staff members is required to manage and perform the adaptations. They manipulate the storyline and the level of support during their role-play of important characters in the scenario. The costs of training could be reduced if the adaptation is automated by using intelligent agent technology to control the characters within a virtual training environment (a serious game). However, such a system also needs a didactical component to monitor the trainee and determine necessary adaptations to the scenario. This paper investigates the automation of didactical knowledge and the corresponding dynamic adaptation of the scenario. A so-called director decides upon and distributes the necessary changes in real-time to the characters. First, the nature and goals of the adaptations are analyzed. Subsequently, the paper introduces a conducted study into the applicability of directable scenarios. Thereafter, an experiment is introduced that investigates the effects of directorial interventions upon the instructive quality of the scenario. Qualitative results indicated that trainees experienced scenario-based training to be instructive and motivating. Moreover, quantitative results showed that instructors rated directed scenarios as significantly better attuned to the trainees needs compared to non-directed scenarios. Our future research will focus at the design of an architecture for automatically directed scenario-based training. Cues relevant for the learning process can be identified in real-time.These cues can trigger scripts to effectively adapt training in real-time.Adapted training scenarios improve the quality of learning.

Scenario-based training: director's cut

Research regarding autonomous learning shows that freeplay does not result in optimal learning. Combining scenario-based training with intelligent agent technology offers the possibility to create autonomous training enriched with automated adaptive support delivered by a director agent. We conducted an experiment to investigate whether directing training scenarios improves the quality of training. Six instructors rated video fragments of directed and non-directed scenarios in terms of learning value. Results show that the instructors consider directed scenarios to be considerably more effective for learning than nondirected scenarios. Implications for the design of a director agent are discussed.

An ontology for automated scenario-based training

An intelligent system for automated scenario-based training SBT needs knowledge about the training domain, events taking place in the simulated environment, the behaviour of the participating characters, and teaching strategies for effective learning. This knowledge base should be theoretically sound and should represent the information in a generic, consistent, and unambiguous manner. Currently, there is no such knowledge base. This paper investigates the declarative knowledge needed for a system to reason about training and to make intelligent teaching decisions. A frame-based approach was used to model the identified knowledge in an ontology. The ontology specifies the core concepts of SBT and their relationships, and is applicable across training domains and applications. The ontology was used to develop a critical component of SBT: the scenario generator. It was found that the ontology enabled the scenario generator to develop scenarios that fitted the learning needs and skill level of the trainee. The presented work is an important step towards automated scenario-based training systems.

Perceived Autonomy of Robots: Effects of Appearance and Context

Due to advances in technology, the world around us contains an increasing number of robots, virtual agents, and other intelligent systems. These systems all have a certain degree of autonomy. For the people who interact with an intelligent system it is important to obtain a good understanding of its degree of autonomy: what tasks can the system perform autonomously and to what extent? In this paper we therefore present a study on how a system’s characteristics affect people’s perception of its autonomy. This was investigated by asking fire-fighters to rate the autonomy of a number of search and rescue robots in different shapes and situations. In this paper, we identify the following seven aspects of perceived autonomy: time interval of interaction, obedience, informativeness, task complexity, task implication, physical appearance, and physical distance to human operator. The study showed that increased disobedience, task complexity and physical distance of a robot can increase perceived autonomy.

Agent-Based Personalisation and User Modeling for Personalised Educational Games

Personalisation can increase the learning efficacy of educational games by tailoring their content to the needs of the individual learner. This paper presents the Personalised Educational Game Architecture (PEGA). It uses a multi-agent organisation and an ontology to offer learners personalised training in a game environment. The multi-agent organisations flexibility enables adaptive automation; the instructor can decide to control only parts of the training, while leaving the rest to the intelligent agents.