In this paper, we describe the experience gained when Evolutionary Algorithms were applied to SATB music composition and the impact it is demonstrating in Spanish Professional Music Conservatories for the last three years. To our knowledge, this is the first time that an EA-inspired tool has been used at a national level in music conservatories.

Personalized hybrid agent-based systems leverage data-driven and symbolic components to provide tailored, context-aware decision support in multiple domains. Yet, the field lacks a consolidated and evidence-based overview of current approaches, their maturity, and the open challenges they present. Method. This study presents a Systematic Literature Review (2018–2025) combining Kitchenham’s protocol with a Goal–Question–Metric (GQM) framework. Searches in peer-reviewed and indexed repositories (e.g., Scopus, Web of Science, and IEEE Xplore) returned 9,733 records, reduced to 46 primary studies after an initial screening. The main research question targets how personalized, agent-based Hybrid AI are currently conceived and implemented. In particular, the study is organized around 10 Structured Research Questions (SRQs) focusing on demographics, abstraction, domains, objectives, users, hybridization, technologies, advantages, limitations, evaluation, and future challenges. Results. Three dominant integration strategies surfaced: (1) concatenated pipelines that serially couple ML outputs to rule engines; (2) shared-representation models that embed symbolic knowledge in neural architectures; and (3) agent-level orchestration where heterogeneous components interact via message passing. While recommendation and adaptive coaching are the main use-cases, 71% of contributions remain at the prototype-level and lack large-scale or longitudinal evaluations. Technical barriers include scalability, semantic interoperability, and explainability; only 15% of studies report user-centered validation. Conclusions. The review reveals a growing but fragmented landscape. The paper proposes a research roadmap calling for: (i) publicly available benchmark datasets for hybrid personalization, (ii) standardized Hybrid Artificial Intelligence protocols for agent-to-human and agent-to-agent explanations, and (iii) design guidelines that combine symbolic guarantees with data-driven adaptability. Addressing these gaps is essential for a trustworthy deployment of Hybrid AI in sensitive settings.

The development of robots for automatic pruning of vineyards using deep learning techniques seems feasible in the medium term. In this context, it is essential to propose and study solutions that can be deployed on portable hardware, with artificial intelligence capabilities but reduced computing power. In this paper, we propose a novel approach to vineyard pruning by direct detection of cutting areas in real time by comparing Mask R-CNN and YOLOv8 performances. The studied object segmentation architectures are able to segment the image by locating the trunk, and pruned and not pruned vine shoots. Our study analyzes the performance of both frameworks in terms of segmentation efficiency and inference times on a Jetson AGX Orin GPU. To compare segmentation efficiency, we used the mAP50 and AP50 per category metrics. Our results show that YOLOv8 is superior both in segmentation efficiency and inference time. Specifically, YOLOv8-S exhibits the best tradeoff between efficiency and inference time, showing an mAP50 of 0.883 and an AP50 of 0.748 for the shoot class, with an inference time of around 55 ms on a Jetson AGX Orin.