A decade after its introduction, Industrie 4.0 has been established globally as the dominant paradigm for the digital transformation of the manufacturing industry. Amalgamating research-based results and practical experience from the German industry, this contribution reviews the progress made in implementing Industrie 4.0 and identifies future fields of action from a technological and application-oriented perspective. Putting the human in the center, Industrie 4.0 is the basis for data-based value creation, innovative business models, and agile forms of organization. Today, in the German manufacturing industry, the Internet of Things and cyber–physical production systems are a reality in newly built factories, and the connectivity of machinery has been significantly increased in existing factories. Now, the trends of industrial AI, edge computing up to the edge cloud, 5G in the factory, team robotics, autonomous intralogistics systems, and trustworthy data infrastructures must be leveraged to strengthen resilience, sovereignty, semantic interoperability, and sustainability. This enables the creation of digital innovation ecosystems that ensure long-term adaptability in a volatile economic and geopolitical environment. In sum, this review represents a comprehensive assessment of the status quo and identifies what is needed in the future to reap the rewards of the groundwork done in the first ten years of Industrie 4.0.

The design of mobile navigation systems adapting to limited resources will be an important future challenge. Since typically several different means of transportation have to be combined in order to reach a destination, the user interface of such a system has to adapt to the user's changing situation. This applies especially to the alternating use of different technologies to detect the user's position, which should be as seamless as possible. This article presents a hybrid navigation system that relies on different technologies to determine the user's location and that adapts the presentation of route directions to the limited technical resources of the output device and the limited cognitive resources of the user.

Multimodal interfaces combining natural language and graphics take advantage of both the individual strength of each communication mode and the fact that several modes can be employed in parallel. The central claim of this paper is that the generation of a multimodal presentation can be considered as an incremental planning process that aims to achieve a given communicative goal. We describe the multimodal presentation system WIP which allows the generation of alternate presentations of the same content taking into account various contextual factors. We discuss how the plan-based approach to presentation design can be exploited so that graphics generation influences the production of text and vice versa. We show that wellknown concepts from the area of natural language processing like speech acts, anaphora, and rhetorical relations take on an extended meaning in the context of multimodal communication. Finally, we discuss two detailed examples illustrating and reinforcing our theoretical claims.

This chapter surveys the field of user modeling in artificial intelligence dialog systems. First, reasons why user modeling has become so important in the last few years are pointed out, and definitions are proposed for the terms 'user model' and 'user modeling component'. Research within and outside of artificial intelligence which is related to user modeling in dialog systems is discussed. In Section 2, techniques for constructing user models in the course of a dialog are presented and, in Section 3, recent proposals for representing a wide range of assumptions about a user's beliefs and goals in a system's knowledge base are surveyed. Examples for the application of user models in systems developed to date are then given, and some social implications discussed. Finally, unsolved problems like coping with collective beliefs or resource-limited processes are investigated, and prospects for applicationoriented research are outlined. Although the survey is restricted to user models in naturallanguage dialog systems, most of the concepts and methods discussed can be extended to AI dialog systems in general.

Abstract.We introduce the notion of symmetric multimodality for dialogue systems in which all input modes (eg. speech, gesture, facial expression) are also available for output, and vice versa. A dialogue system with symmetric multimodality must not only understand and represent the user's multimodal input, but also its own multimodal output. We present the SmartKom system, that provides full symmetric multimodality in a mixed-initiative dialogue system with an embodied conversational agent. SmartKom represents a new generation of multimodal dialogue systems, that deal not only with simple modality integration and synchronization, but cover the full spectrum of dialogue phenomena that are associated with symmetric multimodality (including crossmodal references, one-anaphora, and backchannelling). We show that SmartKom's plug-anplay architecture supports multiple recognizers for a single modality, eg. the user's speech signal can be processed by three unimodal recognizers in parallel (speech recognition, emotional prosody, boundary prosody). Finally, we detail SmartKom's three-tiered representation of multimodal discourse, consisting of a domain layer, a discourse layer, and a modality layer.

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Abstract. Verbmobil is a speaker-independent and bidirectional speech-to-speech translation system for spontaneous dialogs in mobile situations. It recognizes spoken input, analyses and translates it, and finally utters the translation. The multilingual system handles dialogs in three business-oriented domains, with context-sensitive translation between three languages (German, English, and Japanese). Since Verbmobil emphasizes the robust processing of spontaneous dialogs, it poses difficult challenges to human language technology, that we discuss in this paper. We present Verbmobil as a hybrid system incorporating both deep and shallow processing schemes. We describe the anatomy of Verbmobil and the functionality of its main components. We discuss Verbmobil's multi-blackboard architecture that is based on packed representations at all processing stages. These packed representations together with formalisms for underspecification capture the non-determinism in each processing phase, so that the remaining uncertainties can be reduced by linguistic, discourse and domain constraints as soon as they become applicable. We present Verbmobil's multi-engine approach, eg. its use of five concurrent translation engines: statistical translation, case-based translation, substring-based translation, dialog-act based translation, and semantic transfer. Distinguishing features like the multilingual prosody module and the generation of dialog summaries are highlighted. We conclude that Verbmobil has successfully met the project goals with more than 80% of approximately correct translations and a 90% success rate for dialog tasks.