Abstract
The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.

Abstract

Abstract
In this article the application of the discrete version of the bat algorithm to flowshop scheduling problems is presented and compared with Simulated Annealing, Local Search, as well as versions of each that start from constructive heuristics (Palmer and CDS). Bat algorithm is a novel metaheuristic, developed for continuous problems that has shown exceptional results. This paper intends to assess its effectiveness and efficiency for discrete problems when compared with other optimization techniques, including Simulated Annealing and Local Search, whose results are already proven. First, it was developed a literature review about those algorithms, then they were implemented in VBA with Microsoft Excel. Once implemented, the parameterization was carried out, ensuring an adequate application of the algorithms before they can be compared. Then, the methods were applied for 30 normally distributed instances, in order to draw broader conclusions. Finally, a statistical evaluation was carried out and concluded the inferiority of the Local Search in relation to the metaheuristics and the superiority of the hybrid version of the Bat Algorithm with CDS in relation to Simulated Annealing, with significantly better solutions, in an equal computation time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Conflict-free Replicated Data Types (CRDTs) are useful to allow a distributed system to operate on data even when partitions occur, and thus preserve operational availability. Most CRDTs need to track whether data evolved concurrently at different nodes and needs to be reconciled; this requires storing causality metadata that is proportional to the number of nodes. In this paper, we try to overcome this limitation by introducing a stochastic mechanism that is no longer linear on the number of nodes, but whose accuracy is now tied to how much divergence occurs between synchronizations. This provides a new tool that can be useful in deployments with many anonymous nodes and frequent synchronizations. However, there is an underlying trade-off with classic deterministic solutions, since the approach is now probabilistic and the accuracy depends on the configurable metadata space size.

Abstract
This paper presents an innovative digital platform for managing energy communities with self-consumption and energy trading in a local electricity market. Its architecture is based on micro-services, such as the energy transaction service, the settlement service to compute the financial compensations among community members for the energy transacted, or a resource sizing service. This approach enables the platform to be more efficient and scalable, making easier to incorporate new functionalities while maintaining a secure community and energy transactions management. The transactions and settlement procedures, adapted to the Portuguese regulation, are described, and the results of the platform operating a post-delivery pool market are presented and analyzed. This paper contributes to the understanding and improvement of renewable energy communities' business models and management, offering insights for policymakers, researchers, and practitioners in the field.

Abstract
This paper presents the TURTLE hybrid robotic lander in the context of the field trials performed in the REP(MUS) 2022 military exercise. The TURTLE robot combines the characteristics and mobility of an autonomous underwater vehicle with the ones of a seabed lander, having been designed for extended permanence on the sea bottom and efficient ascending and dive to the deep sea. The REP( MUS) 2022 exercises organized by the Portuguese navy in collaboration with NATO organizations and other institutions demonstrated the large-scale use of unmanned marine systems in an operational scenario. The robotic system is presented as well as some of the results and experience from the field trials.