The AI4Culture project, funded by the European Commission (2023-2025), developed a platform (https://ai4culture.eu) to educate cultural heritage (CH) professionals in AI technologies. Acting as an online capacity building hub, the platform describes openly labeled data sets and deployable and reusable tools applying AI technologies in tasks relevant to the CH sector. It also offers tutorials for tools and recipes for the combination of tools. In addition, the platform allows users to contribute their own resources. The resources described by project partners involve applications for optical or handwritten character recognition (OCR, HTR), generation and validation of subtitles, machine translation, image analysis, and semantic linking. The partners customized various tools to enhance the usability of interfaces and components. Here, we zoom in on the use case of correcting OCR/HTR output using various means (such as an unstructured manual transcription) to facilitate multilingual accessibility and create structured ground truth (text lines with image coordinates).

Europeana Translate is a project funded under the Connecting European Facility with the objective to take advantage of state-of-the-art machine translation in order to increase the multilinguality of resources in the cultural heritage domain

Domotics, concerned with the realization of intelligent home environments, is a novel field which can highly benefit from solutions inspired by service-oriented principles to enhance the convenience and security of modern home residents. In this work, we present an architecture for a smart home, starting from the lower device interconnectivity level up to the higher application layers that undertake the load of complex functionalities and provide a number of services to end-users. We claim that in order for smart homes to exhibit a genuinely intelligent behavior, the ability to compute compositions of individual devices automatically and dynamically is paramount. To this end, we incorporate into the architecture a composition component that employs artificial intelligence domain-independent planning to generate compositions at runtime, in a constantly evolving environment. We have implemented a fully working prototype that realizes such an architecture, and have evaluated it both in terms of performance as well as from the end-user point of view. The results of the evaluation show that the service-oriented architectural design and the support for dynamic compositions is quite efficient from the technical point of view, and that the system succeeds in satisfying the expectations and objectives of the users.

Web Service (WS) domains constitute an application field where automated planning can significantly contribute towards achieving customisable and adaptable compositions. Following the vision of using domain-independent planning and declarative complex goals to generate compositions based on atomic service descriptions, we apply a planning framework based on Constraint Satisfaction techniques to a domain consisting of WSs with diverse functionalities. One of the key requirements of such domains is the ability to address the incomplete knowledge problem, as well as recovering from failures that may occur during execution. We propose an algorithm for interleaving planning, monitoring and execution, where continual planning via altering the CSP is performed, under the light of the feedback acquired at runtime. The system is evaluated against a number of scenarios including real WSs, demonstrating the leverage of situations that can be effectively tackled with respect to previous approaches.