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Smart devices, such as smart phones, voice assistants and social robots, provide users with a range of input modalities, e.g., speech, touch, gestures, and vision. In recent years, advancements in processing of these input channels enable more natural interaction (e.g., automated speech, face, and gesture recognition, dialog generation, emotion expression etc.) experiences for users. However, there are several important challenges that need to be addressed to create these user experiences. One challenge is that most smart devices do not have sufficient computing resources to execute the Artificial Intelligence (AI) techniques locally. Another challenge is that users expect responses in near real-time when they interact with these devices. Moreover, users also want to be able to seamlessly switch between devices and services any time and from anywhere and expect personalized and privacy-aware services. To address these challenges, we design and develop a cloud-based middleware (CMI) which helps to develop multi-modal interaction applications and easily integrate applications to AI services. In this middleware, services developed by different producers with different protocols and smart devices with different capabilities and protocols can be integrated easily. In CMI, applications stream data from devices to cloud services for processing and consume the results. It supports data streaming from multiple devices to multiple services (and vice versa). CMI provides an integration framework for decoupling the services and devices and enabling application developers to concentrate on 'interaction' instead of AI techniques. We provide simple examples to illustrate the conceptual ideas incorporated in CMI.

Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor. Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom.

In this paper we specify and validate three interaction design patterns for an interactive storytelling experience with an autonomous social robot. The patterns enable the child to make decisions about the story by talking with the robot, reenact parts of the story together with the robot, and recording self-made sound effects. The design patterns successfully support children's engagement and agency. A user study (N = 27, 8-10 y.o.) showed that children paid more attention to the robot, enjoyed the storytelling experience more, and could recall more about the story, when the design patterns were employed by the robot during storytelling. All three aspects are important features of engagement. Children felt more autonomous during storytelling with the design patterns and highly appreciated that the design patterns allowed them to express themselves more freely. Both aspects are important features of children's agency. Important lessons we have learned are that reducing points of confusion and giving the children more time to make themselves heard by the robot will improve the patterns efficiency to support engagement and agency. Allowing children to pick and choose from a diverse set of stories and interaction settings would make the storytelling experience more inclusive for a broader range of children.

Contains fulltext : 209052.pdf (publisher's version ) (Open Access)

In order to facilitate a sustainable long-term interaction between a child and a robot they need to get acquainted with one another. In this paper we discuss the foundation, the rationale, and the evaluation (N = 75) of our design for an autonomous robot conversational partner that engages with Dutch children (8-11 y.o.) in a getting acquainted interaction. The main objective of the robot is to elicit children to self-disclose. Firstly, we discuss five interaction design patterns (IDPs) that proved to be successful in autonomously eliciting and processing self-disclosures. Secondly, we compared two robot behavior profiles. The behavior profiles can be relatively considered as being more and less energetic. We manipulated the movement speed, the speech rate and volume, the use of high/low energy language, waiting time before responding, and the order of high/low energy activities. Results show that the less energetic behavior profile significantly leads to more self-disclosure.

Robots y la forma en la que interactúan con personas, en los últimos años, ha sufrido un increíble crecimiento. En particular por la popularidad que están obteniendo por su uso en museos, centros de congresos, aeropuertos… El problema principal es que su software es aun no suficientemente funcional y no es capaz de ofrecer un buen rendimiento. El objetivo de esta investigación es mejorar las capacidades de Pepper en el campo de detección de personas y estimación de distancia. Para hacerlo usaremos un nuevo componente de hardware que Pepper integra en esta última versión, las visión en estéreo. Usaremos OpenCV y tensorFlow para procesar las imágenes. Compararemos las mejoras que nuestra técnica tiene frente a los métodos integrados en Pepper para estimar distancia a personas.---ABSTRACT---Robotics, and the way that they interact with people, in the last years, has seen incredible growth, in particular for the popularization that they are getting in museums, congress centers, airports. The main problem is that its software is still clumsy and not capable of offering a good performance. The aim of this research is to improve Pepper capabilities in the field of people detection and distance estimation. For doing this we will use one of the new hardware components that Pepper provides, Stereo Vision. We will make use of OpenCV and tensorFlow for processing the images. We compare the improvements that our technique has with the built-in methods of pepper robot for distance estimation and people detection.

This report documents the programme and outcomes of Dagstuhl Seminar 12342 ``Engineering multiagent Systems''. The seminar brought together researchers from both academia and industry to identify the potential for and facilitate convergence towards standards for agent technology. As such it was particularly relevant to industrial research. A key objective of the seminar, moreover, has been to establish a road map for engineering multiagent systems. Various research areas have been identified as important topics for a research agenda with a focus on the development of multiagent systems. Among others, these include the integration of agent technology and legacy systems, component-based agent design, standards for tooling, establishing benchmarks for agent technology, and the development of frameworks for coordination and organisation of multiagent systems. This report presents a more detailed discussion of these and other research challenges that were identified. The unique atmosphere of Dagstuhl provided the perfect environment for leading researchers from a wide variety of backgrounds to discuss future directions in programming languages, tools and platforms for multiagent systems, and the road map produced by the seminar will have a timely and decisive impact on the future of this whole area of research.

This report documents the programme and outcomes of Dagstuhl Seminar 12342 ``Engineering multiagent Systems''. The seminar brought together researchers from both academia and industry to identify the potential for and facilitate convergence towards standards for agent technology. As such it was particularly relevant to industrial research. A key objective of the seminar, moreover, has been to establish a road map for engineering multiagent systems. Various research areas have been identified as important topics for a research agenda with a focus on the development of multiagent systems. Among others, these include the integration of agent technology and legacy systems, component-based agent design, standards for tooling, establishing benchmarks for agent technology, and the development of frameworks for coordination and organisation of multiagent systems. This report presents a more detailed discussion of these and other research challenges that were identified. The unique atmosphere of Dagstuhl provided the perfect environment for leading researchers from a wide variety of backgrounds to discuss future directions in programming languages, tools and platforms for multiagent systems, and the road map produced by the seminar will have a timely and decisive impact on the future of this whole area of research.

This article describes research in a new theory of decision support in negotiation in family law mediation. AssetDivider was based on the principles of Family_Winner. As a Negotiation Decision Support System Family_Winner takes ratings assigned to items by the parties involved and develops a list of allocations to each party; based on trade-offs inherently present in the dispute. Given advice provided from our industry partners Relationships Australia (Queensland) - RAQ, AssetDivider uses an ideal “percentage split” to guide the development of an allocation list for parties. The system has been tested informally by our contacts at RAQ, and we now look forward to extensive testing and evaluation by mediators at RAQ in the near future. We hope to report on a comprehensive evaluation which will report on the effectiveness of this program in practice.