The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version

An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms

In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The ArchAIDE project realised an AI-based application to recognise archaeological pottery. Pottery is of paramount importance for understanding archaeological contexts. However, recognition of ceramics is still a manual, time-consuming activity, reliant on analogue catalogues. The project developed two complementary machine-learning tools to propose identifications based on images captured on-site, for optimising and economising this process, while retaining key decision points necessary to create trusted results. One method relies on the shape of a potsherd; the other is based on decorative features. For the shape-based recognition, a novel deep-learning architecture was employed, integrating shape information from points along the inner and outer profile of a sherd. The decoration classifier is based on relatively standard architectures used in image recognition. In both cases, training the algorithms meant facing challenges related to real-world archaeological data: the scarcity of labelled data; extreme imbalance between instances of different categories; and the need to take note of minute differentiating features. Finally, the creation of a desktop and mobile application that integrates the AI classifiers provides an easy-to-use interface for pottery classification and storing pottery data

Na transição entre os séculos XX e XXI: interseções e sobreposições entre educação e criatividade nos Museus

A museologia dividiu a história dos museus em períodos sucessivos e sequenciais,pautados por momentos de continuidade e ruturas. No uir da história dos museus, porém, osacontecimentos intersectam-se e coexistem, o que exige um olhar uido e não segmentado.Este estudo aborda o espaço de transição entre o nal do século XX período em que a educaçãonos museus se tornou mais transversal e o início do século XXI, em que a criatividade procuraentrar nas práticas dos museus. Ao abordar este espaço de transição pretende-se sublinhar aimportância de não criar fronteiras rígidas entre tempos e áreas, para que a criatividade aconteçanos espaços de transição.Esta reexão evidencia que o desvanecer de fronteiras e a abordagem integrada e líquida de diferentesrealidades contribui para a construção do conhecimento, pois uma visão plurifacetada ecomplexa potencia um melhor entendimento da realidade.Museology has divided the history of museums in successive and sequential periods,guided by moments of continuity and ruptures. In the ow of the museums history, however,events intersect and overlap ones with the others, which require a uid and non-segmented look.is research addresses the space of transition between the end of the twentieth century period inwhich education in museums became more transversal and the beginning of the XXI century, in whichcreativity comes deeper into the practices of museums. is study aims to underline the importance ofnot creating rigid boundaries between times and areas, so that creativity happens in transitional spaces.is reection shows that the fading of boundaries and the integrated approach of dierent realitiescontributes to the construction of knowledge. It also demonstrates that a multifaceted and complexview enhances a better understanding of reality

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade