In human-robot collaboration scenarios, mutual adaptation between the human and robot must occur to ensure high task performance. This requires robotic systems to be capable of reasoning based on a long-term history of interactions. In this paper, we present and evaluate a robot simulation system that facilitates adaptive robot behavior using ontology-based reasoning and behavior trees in an interactive robotic scanning task. A study with 38 participants compares our adaptive system with a static system in team performance and perceived system usability. Our results suggest that use of the adaptive system significantly reduced session time, leading users to perform the task 19.5% faster. Furthermore, participants reported significantly lower fatigue levels, while maintaining the same task performance as those using the static system.

Despite the recent advancements in speech recognition, there are still difficulties in accurately transcribing conversational and emotional speech in noisy and reverberant acoustic environments. This poses a particular challenge in the search and rescue (SAR) domain, where transcribing conversations among rescue team members is crucial to support real-time decision-making. The scarcity of speech data and associated background noise in SAR scenarios make it difficult to deploy robust speech recognition systems.To address this issue, we have created and made publicly available a German speech dataset called RescueSpeech. This dataset includes real speech recordings from simulated rescue exercises. Additionally, we have released competitive training recipes and pre-trained models. Our study highlights that the performance attained by state-of-the-art methods in this challenging scenario is still far from reaching an acceptable level.

Making the transition to long-term interaction with social-robot systems has been identified as one of the main challenges in human-robot interaction. This article identifies four design principles to address this challenge and applies them in a real-world implementation: cloud-based robot control, a modular design, one common knowledge base for all applications, and hybrid artificial intelligence for decision making and reasoning. The control architecture for this robot includes a common Knowledge-base (ontologies), Data-base, “Hybrid Artificial Brain” (dialogue manager, action selection and explainable AI), Activities Centre (Timeline, Quiz, Break and Sort, Memory, Tip of the Day, \( \ldots \) ), Embodied Conversational Agent (ECA, i.e., robot and avatar), and Dashboards (for authoring and monitoring the interaction). Further, the ECA is integrated with an expandable set of (mobile) health applications. The resulting system is a Personal Assistant for a healthy Lifestyle (PAL), which supports diabetic children with self-management and educates them on health-related issues (48 children, aged 6–14, recruited via hospitals in the Netherlands and in Italy). It is capable of autonomous interaction “in the wild” for prolonged periods of time without the need for a “Wizard-of-Oz” (up until 6 months online). PAL is an exemplary system that provides personalised, stable and diverse, long-term human-robot interaction.

This paper describes the use of Multi-Task Neural Networks (NNs) for system dialogue act selection.These models leverage the representations learned by the Natural Language Understanding (NLU) unit to enable robust initialization/bootstrapping of dialogue policies from medium sized initial data sets.We evaluate the models on two goal-oriented dialogue corpora in the travel booking domain.Results show the proposed models improve over models trained without knowledge of NLU tasks.

This paper describes ongoing work carried out in the European project PAL which will support childre in their diabetes self-management as well as assist health professionals and parents involved in the diabete regimen of the child. Here, we will focus on the construction of the PAL ontology which has been assemble from several independently developed sub-ontologies and which are brought together by a set of hand-writte interface axioms, expressed in OWL.We will describe in detail how the triple model of RDF has been extende towards transaction time in order to represent time-varying data. Examples of queries and rules involvin temporal information will be presented as well. The approach is currently been in use in diabetes camps. Copyright © 2016.Fundacao para a Ciencia e Tecnologia (FCT); Institute for Systems and Technologies of Information, Control and Communication (INSTICC)

This video paper describes the practical outcome of the first milestone of a project aiming at setting up a so-called Hybrid Team that can accomplish a wide variety of different tasks. In general, the aim is to realize and examine the collaboration of augmented humans with autonomous robots, virtual characters and SoftBots (purely software based agents) working together in a Hybrid Team to accomplish common tasks. The accompanying video shows a customized packaging scenario and can be downloaded from http://hysociatea.dfki.de/?p=441.

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.

This paper presents the results from an experiment with a conversational human-robot interaction system aimed at long-term support for diabetic children. The system offers a set of activities aimed to help a child to improve its capability to manage diabetes. There is a large body of literature on the techniques that artificial agents can use to establish and maintain long-term social-emotional relationships with their users. The novel aspect in the present study is the inclusion of off-activity talk interspersed within talk pertaining the activity at hand and aimed to elicit the child's self-disclosure. The children in our study (N=20, age 11-14) were more interested to have another session with the robot when their interaction included also off-activity talk, even though there was no difference in the perception of the robot by the children between the groups with and without off-activity talk. Furthermore, individual interactions with the robot positively influenced the children's adherence to a therapy-related requirement, namely the filling in of a nutritional diary.

We describe an implemented hybrid reasoning architecture that is used in an EU-funded project called MUSING (www.musing.eu) which is dedicated towards the investigation of semantic-based business intelligence solutions. The reasoning platform builds on publicly available software, such as Pellet, OWLIM, Jena, and Sesame. The project uses and extends existing OWL ontologies (e.g., PROTON) and assumes rule-based reasoning to take place on top of OWL. We describe the pros and cons of each subsystem w.r.t. the needs we have encountered during our investigation. We explain the specific reasoning architecture that is based on a sequence and a fixpoint computation of three reasoners which we might sloppily write as Pellet + (OWLIM + Jena)^*. Pellet is used for checking the initial consistency of the ontology, whereas OWLIM and Jena are employed to execute rules outside the expressiveness of OWL. However, OWLIM is way much faster than Jena, but neither has means to do numerical comparison nor arithmetic. We explain our choice why SWRL is not enough and why we believe that binary OWL properties lead to an unwanted proliferation of objects, making representation and reasoning extremely complex.

We present a novel, data-driven method for integrated shallow and deep parsing. Mediated by an XML-based multi-layer annotation architecture, we interleave a robust, but accurate stochastic topological field parser of German with a constraint-based HPSG parser. Our annotation-based method for dovetailing shallow and deep phrasal constraints is highly flexible, allowing targeted and fine-grained guidance of constraint-based parsing. We conduct systematic experiments that demonstrate substantial performance gains.

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis.

We describe a novel method for coping with ungrammatical input based on the use of chart-like data structures, which permit anytime processing. Priority is given to deep syntactic analysis. Should this fail, the best partial analyses are selected, according to a shortest-paths algorithm, and assembled in a robust processing phase. The method has been applied in a speech translation project with large HPSG grammars.

This paper describes new and improved techniques which help a unification-based parser to process input efficiently and robustly. In combination these methods result in a speed-up in parsing time of more than an order of magnitude. The methods are correct in the sense that none of them rule out legal rule applications.

We present an implemented compilation algorithm that translates HPSG into lexicalized feature-based TAG, relating concepts of the two theories. While HPSG has a more elaborated principle-based theory of possible phrase structures, TAG provides the means to represent lexicalized structures more explicitly. Our objectives are met by giving clear definitions that determine the projection of structures from the lexicon, and identify "maximal" projections, auxiliary trees and foot nodes.