Over the last several years, the market for speech technology has seen significant developments (Pieraccini and Huerta We learned some lessons which we use as guidelines 2005) and powerful commercial off-the-shelf solutions for in the development of multimodal dialogue systems where speech recognition (ASR) or speech synthesis (TTS). Further users can combine speech and gestures when using multiple application scenarios, more diverse and dynamic information interaction devices. In earlier projects (Wahlster 2003; Reithinger sources, and more complex prototype systems need et al. 2005) we integrated different sub-components to be addressed in the context of QA. Dialogue-based QA allows to multimodal interaction systems. Other lessons served as a user to pose questions in natural speech, followed by guidelines in the development of semantic dialogue systems answers presented in a concise form (Sonntag et al. 2007).

A frequent topic discussed in the Linked Data community, especially when trying to outreach its values, is "What can we do with all this data ?". In this paper, we demonstrate (1) how to measure semantic distance on Linked Data in order to identify relatedness between resources, and (2) how such measures can be used to provide a new kind of self-explanatory recommendations, bringing together Linked Data and Artificial Intelligence principles, and demonstrating how intelligent agents could emerge in the realm of Linked Data.

Many of the benefits of structured data come about when users can re-use existing vocabularies rather than create new ones, but it is currently difficult for users to find, create, and host new vocabularies. Moreover, the value of any given vocabulary as a foundation for applications depends on the perceived certainty that the vocabulary — both its machine-readable schemas and human-readable specification documents — will remain reliably accessible over time and that its URIs will not be sold, re-purposed, or simply forgotten. This note proposes two approaches for solving these problems: one for multiple Vocabulary Hosting Services and a Vocabulary Preservation System to keep them linked together.

The Web of Data (WoD) is growing at an amazing rate and it will no longer be feasible to deal with it in a global way, by centralising the data or reasoning processes making use of that data. We believe that Computational Intelligence techniques provides the adaptiveness, robustness and scalability that will be required to exploit the full value of ever growing amounts of dynamic Semantic Web data.

We introduce a search engine and information retrieval system for providing access to linked opinion data. Natural language technology of generalization of syntactic parse trees is introduced as a similarity measure between subjects of textual opinions to link them on the fly. Information extraction algorithm for automatic summarization of web pages in the format of Google sponsored links is presented. We outline the usability of the implemented system, integrated opinion delivery environment (IODE).

Semantic technologies are available, and gain popularity on the Web as well as on the desktop, but both (desktop and Web) act as large data silos, making personal and online data difficult to interlink. We propose a system that enables easy publishing of personal notes as linked social media content, while at the same time semantically enriching the desktop resources with information retrieved from the Linked Data cloud. The transformation, publication and linking process is integrated with the familiar desktop applications and online blogging platforms, providing a better usability experience.

Anticipating the actions of others is key to coordinating joint activities. We propose the notion of anticipatory action and perception for for robots acting with humans. We describe four systems in which anticipation has been modeled for human-robot interaction; two in a teamwork setting, and two in a human-robot joint performance setting. In evaluating the effects of anticipatory agent activity, we find in one study that anticipation aids in team efficiency, as well as in the perceived commitment of the robot to the team and its contribution to the team's fluency and success. In another study we see anticipatory action and perception affect the human partner's sense of team fluency, the team's improvement over time, the robot’s contribution to the efficiency and fluency, the robot's intelligence, and the robot’s adaptation to the task. We also find that subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence.

We propose a methodology for automatic synthesis of embedded control software that accounts for exogenous disturbances. The resulting system is guaranteed, by construction, to satisfy a given specification expressed in linear temporal logic. The embedded control software consists of three components: a goal generator, a trajectory planner, and a continuous controller. We demonstrate the effectiveness of the proposed technique through an example of an autonomous vehicle navigating an urban environment. This example also illustrates that the system is not only robust with respect to exogenous disturbances but also capable of handling violation of the environment assumptions.

A Digital Product Memory provides a digital diary of the complete product life cycle that is embedded in the product itself using smart wireless sensor technology. The data is hereby gathered by recording relevant ambient parameters in digital form. In this paper, we present the architecture and cost-efficient implementation of an autonomous digital product memory that generates and interprets its diary using rule-based reasoning methods. As we assume an open, heterogeneous sensor infrastructure, we rely on standard syntax and semantics provided by the Web Ontology Language OWL. The digital product memory collects and provides data using the OWL fragment OWL2 RL which can be processed with standard rule engines. As rule engine we use CLIPS on embedded hardware and exemplify the application of the digital product memory e.g. for predictive maintenance.

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.