Single Value Devices

We live in a world of continuous information overflow, but the quality of information and communication is suffering. Single value devices contribute to the information and communication quality by fo- cussing on one explicit, relevant piece of information. The information is decoupled from a computer and represented in an object, integrates into daily life. However, most existing single value devices come from conceptual experiments or art and exist only as prototypes. In order to get to mature products and to design meaningful, effective and work- ing objects, an integral perspective on the design choices is necessary. Our contribution is a critical exploration of the design space of single value devices. In a survey we give an overview of existing examples. The characterizing design criteria for single value devices are elaborated in a taxonomy. Finally, we discuss several design choices that are specifically important for moving from prototypes to commercializable products

Physical extracurricular activities in educational child-robot interaction

In an exploratory study on educational child-robot interaction we investigate the effect of alternating a learning activity with an additional shared activity. Our aim is to enhance and enrich the relationship between child and robot by introducing "physical extracurricular activities". This enriched relationship might ultimately influence the way the child and robot interact with the learning material. We use qualitative measurement techniques to evaluate the effect of the additional activity on the child-robot relationship. We also explore how these metrics can be integrated in a highly exploratory cumulative score for the relationship between child and robot. This cumulative score suggests a difference in the overall child-robot relationship between children who engage in a physical extracurricular activity with the robot, and children who only engage in the learning activity with the robot.Comment: 5th International Symposium on New Frontiers in Human-Robot Interaction 2016 (arXiv:1602.05456

Exploiting `Subjective' Annotations

Many interesting phenomena in conversation can only be annotated as a subjective task, requiring interpretative judgements from annotators. This leads to data which is annotated with lower levels of agreement not only due to errors in the annotation, but also due to the differences in how annotators interpret conversations. This paper constitutes an attempt to find out how subjective annotations with a low level of agreement can profitably be used for machine learning purposes. We analyse the (dis)agreements between annotators for two different cases in a multimodal annotated corpus and explicitly relate the results to the way machine-learning algorithms perform on the annotated data. Finally we present two new concepts, namely `subjective entity' classifiers resp. `consensus objective' classifiers, and give recommendations for using subjective data in machine-learning applications.\u

Annotations and subjective machines of annotators, embodied agents, users, and other humans

The usual practice in assessing whether a multimodal annotated corpus is fit for purpose is to calculate the level of inter-annotator agreement, and when it exceeds certain fixed threshold the data is considered to be of tolerable quality. There are two problems with this approach. Firstly, it depends on the assumption that any disagreement in the data is not systematic. This assumption may not always be warranted.\ud Secondly, the approach is not well suited for annotations that are subjective to a certain degree. In that case annotator disagreement is (partly) an inherent property of the annotation, expressing something about the level of intersubjectivity between annotators in how they interpret certain communicative behavior versus the amount of idiosyncrasy in their judgements with respect to this behavior.\ud This thesis addresses both problems. In the theoretical part, it is shown that when disagreement is systematic, obtaining a certain level of inter-annotator agreement may not be a guarantee for the data being fit for purpose. Simulations are used to investigate the effect of systematic disagreement on the relation between the level of inter-annotator agreement and the validity of machine-learning results obtained on the data. In the practical part, two new methods are explored for working with data that has been annotated with a low level of inter-annotator agreement. One method is aimed at finding a subset of the annotations that has been annotated more reliably, in a way that makes it possible to determine for new, unseen data whether it should belong to this subset — and therefore, whether a classifier trained on this more reliable subset is qualified to make a judgement for the new data. The other method is designed to use machine learning for explicitly modeling the overlap and disjunctions in subjective judgements of different annotators. Both methods put together should in theory make it possible to build classifiers that, when deployed in a practical application, yield decisions that make sense for the human end user of the application, who indeed also may have his or her own way of interpreting the communicative behavior that is subjected to the classifier

A Demonstration of Continuous Interaction with Elckerlyc

We discuss behavior planning in the style of the SAIBA framework for continuous (as opposed to turn-based) interaction. Such interaction requires the real-time application of minor shape or timing modifications of running behavior and anticipation of behavior of a (human) interaction partner. We discuss how behavior (re)planning and on-the-fly parameter modification fit into the current SAIBA framework, and what type of language or architecture extensions might be necessary. Our BML realizer Elckerlyc provides flexible mechanisms for both the specification and the execution of modifications to running behavior. We show how these mechanisms are used in a virtual trainer and two turn taking scenarios

Establishing Rapport with a Virtual Dancer

We discuss an embodied agent that acts as a dancer and invites human partners to dance with her. The dancer has a repertoire of gestures and moves obtained from inverse kinematics and motion capturing that can be combined in order to dance both on the beat of the music that is provided to the dancer and sensor input (visual and dance pad) from a human partner made available to the virtual dancer. The interaction between virtual dancer and human dancer allows alternating ‘lead’ ad ‘follow’ behavior, both from the point of view of the virtual and the human dancer

Meaning in life as a source of entertainment

In this paper we mean to introduce into the field of entertainment computing an overview of insights concerning fundamental human needs. Researchers such as Hassenzahl and Desmet, discuss design approaches based on psychological insights from various and varied sources. We collect these and expand them with a focus on meaning in life as seen in humanistic philosophy. We summarise the various roles that these insights can play in our research on new technology, and illustrate the discussion with examples from the field of computer entertainment