Transferring research from the laboratory to mainstream applications requires the convergence of people, knowledge, and conventions from divergent disciplines. Solutions involve more than combining functional requirements and creative novelty. To transform technical capabilities of emerging mixed reality (MR) technology into the mainstream involves the integration and evolution of unproven systems. For example, real-world applications require complex scenarios (a content issue) involving an efficient iterative pipeline (a production issue) and driving the design of a story engine (a technical issue) that provides an adaptive experience with an after-action review process (a business issue). This article describes how a multi-disciplinary research team transformed core MR technology and methods into diverse urban terrain applications. These applications are used for military training and situational awareness, as well as for community learning to significantly increase the entertainment, educational, and satisfaction levels of existing experiences in public venues.

Automatic emotion recognition plays a central role in the technologies underlying social robots, affect-sensitive human computer interaction design and affect-aware tutors. Although there has been a considerable amount of research on automatic emotion recognition in adults, emotion recognition in children has been understudied. This problem is more challenging as children tend to fidget and move around more than adults, leading to more self-occlusions and non-frontal head poses. Also, the lack of publicly available datasets for children with annotated emotion labels leads most researchers to focus on adults. In this paper, we introduce a newly collected multimodal emotion dataset of children between the ages of four and fourteen years old. The dataset contains 1102 audio-visual clips annotated for 17 different emotional states: six basic emotions, neutral, valence and nine complex emotions including curiosity, uncertainty and frustration. Our experiments compare unimodal and multimodal emotion recognition baseline models to enable future research on this topic. Finally, we present a detailed analysis of the most indicative behavioral cues for emotion recognition in children.

Biological brains can adapt and learn from past experience. Yet neuroevolution, that is, automatically creating artificial neural networks (ANNs) through evolutionary algorithms, has sometimes focused on static ANNs that cannot change their weights during their lifetime. A profound problem with evolving adaptive systems is that learning to learn is highly deceptive. Because it is easier at first to improve fitness without evolving the ability to learn, evolution is likely to exploit domain-dependent static (i.e., nonadaptive) heuristics. This article analyzes this inherent deceptiveness in a variety of different dynamic, reward-based learning tasks, and proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely. A series of experiments and an in-depth analysis show how behaviors that could potentially serve as a stepping stone to finding adaptive solutions are discovered by novelty search yet are missed by fitness-based search. The conclusion is that novelty search has the potential to foster the emergence of adaptive behavior in reward-based learning tasks, thereby opening a new direction for research in evolving plastic ANNs.

The lack of compelling content has relegated many promising entertainment technologies to laboratory curiosities. Although mixed-reality techniques show great potential, the entertainment business is not about technology. To penetrate these huge markets, MR technology must become transparent for the content to have full effect. To achieve this goal, we have devised a framework that lets us integrate concepts from disparate areas such as theme parks, theater, and film into a comprehensive research methodology. We believe that our framework, which has already helped us create content for MR entertainment systems, can provide these benefits to other developers as well.