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Smart cities generally aim at efficiently organizing and managing city resources through a digital layer on top of the legacy infrastructure. As the digitalization trend goes on with an increasing pace and with the involvement of a diverse set of actors, proper management of this digital layer as well as the services deployed over it becomes ever more crucial. This paper presents our methodology of transforming the complex smart city concept and its digital layer into a structured model for creating a dynamic and adaptive service ecosystem in the digital cities of the future. An overview of the smart city concept and the three-tier architecture that emerges through the digitalization of cities is presented first, together with the main challenges in attaining coherent smart city service environments that can avoid fragmentation, ensure scalability, and allow reuse. The three major enablers that are identified in this direction are 1) a semantic functional description of city objects, representing physical devices or abstract services; 2) a distributed service directory that embodies available city services for service lookup and discovery; and 3) planning tools for selecting and chaining basic services to compose new complex services. For each of these, a highlevel overview of the available tools and results from the research literature are provided as well as the relevant standards. This overview is complemented with our own approach and design choices in project ISCO (Internet of smart city objects). Through the implementation of two distinct use cases, this paper illustrates how ISCO components can jointly enrich service creation and consumption of various stakeholders in smart cities.

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A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot’s real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human’s changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human’s workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human-adaptation is a key to the long-term use of cobots.