Contemporary Arabic narrative generation faces a critical cultural authenticity crisis, where neural models systematically violate classical adab principles despite sophisticated linguistic capabilities. We introduce CST-Onto, a hybrid neuro-symbolic framework addressing this challenge through the first domain-specific OWL/SWRL ontology for Arabic cultural narratives. Our four-tier ontology encodes 87 core cultural archetypes from expert annotations of Kalīla wa Dimna and One Thousand and One Nights [13], expanded to 847 specialized concepts through systematic derivation protocols. The framework formalizes 234 SWRL rules modeling virtue dependencies (e.g., ḥakīm → ḥilm, ʿadl, faṣāḥa) that directly enhance symbolic–neural integration via constraint-aware neural decoding.

Arabic text adaptation confronts severe computational barriers from morphological complexity and dialectal fragmentation. Current graph-based methods employ static representations that fail to capture evolving morphological dependencies, causing substantial semantic degradation (28.0% cross-dialectal error rate) across dialectal boundaries. We introduce MM-GTA (Morphological Modeling via Graph-based Text Adaptation), the first dynamic-graph framework for Arabic style transfer that jointly optimizes morphological dynamism, dialect-conditioned attention, and cultural-adaptive resource fusion (dialect-aware embedding compression). MM-GTA introduces dynamic graph reconstruction and dialect-conditioned attention, reducing cross-dialect errors by 55.8% versus AraT5 (from 28.0% to 12.3%). Evaluation on ArabicStyleBank (10,000 parallel texts across five dialects) demonstrates substantial gains: BLEU 41.2±0.8 (+6.5 over AraBERT’s 34.7±0.9), ROUGE-L 71.8±1.0 (+6.1 points), BERTScore 78.2±1.0 (+4.3 points). The architecture achieves 51% memory reduction with 89ms average inference latency per sentence (vs. AraT5’s 142ms), enabling efficient deployment within 8.0GB memory constraints. Human evaluation by 25 native speakers (5 per dialect, avg. 10 years NLP experience) confirms superior preservation across style authenticity (4.2/5), semantic fidelity (4.3/5), and grammatical accuracy (4.1/5). Our model achieves +17.2% improvement in cultural alignment compared to AraGPT2 across three major dialect families.

Interactive storytelling systems have become very popular as they engage users in the creation of narrative. A fundamental challenge for such systems is that the users feel unconstrained in their exploration of the environment and yet retain for the author some control of what the user does. Traditional solutions address this challenge by hardening the authoring task, since much effort has to be devoted to create the story and the necessary mechanisms to provide the user with enough freedom that, at the same time, keeps the story consistent. We propose a solution based on dynamic narration that combines a set of authored materials associated with particular points in the environment and the exploratory movements of the user, and includes statements of varying emphasis to guide the user towards the desired elements. This allows the system to generate a narration in real time while giving the user the possibility to decide what to do. The solution has been field tested and results are reported on three different aspects: the freedom of the user in terms of combinations in which the authored material is traversed; the divergence of user explorations from author's intentions; and user response to guidance statements of differing emphasis.

This paper addresses the task of generating descriptions of people for an observer that is moving within a scene.As the observer moves, the descriptions of the people around him also change.A referring expression generation algorithm adapted to this task needs to continuously monitor the changes in the field of view of the observer, his relative position to the people being described, and the relative position of these people to any landmarks around them, and to take these changes into account in the referring expressions generated.This task presents two advantages: many of the mechanisms already available for static contexts may be applied with small adaptations, and it introduces the concept of changing conditions into the task of referring expression generation.In this paper we describe the design of an algorithm that takes these aspects into account in order to create descriptions of people within a 3D virtual environment.The evaluation of this algorithm has shown that, by changing the descriptions in real time according to the observers point of view, they are able to identify the described person quickly and effectively.

Describing people and characters can be very useful in different contexts, such as computational narrative or image description for the visually impaired.However, a review of the existing literature shows that the automatic generation of people descriptions has not received much attention.Our work focuses on the description of people in snapshots from a 3D environment.First, we have conducted a survey to identify the way in which people describe other people under different conditions.We have used the information extracted from this survey to design several Referring Expression Generation algorithms which produce similar results.We have evaluated these algorithms with users in order to identify which ones generate the best description for specific characters in different situations.The evaluation has shown that, in order to generate good descriptions, a combination of different algorithms has to be used depending on the features and situation of the person to be described.

During the last years, intelligent virtual environments for training have become a quite popular application of computer science to education. However, little attention is being paid to software engineering issues, and most systems are developed in an ad-hoc way that does not allow the reuse of their components or an easy modification of the application, even though some authors claim that the use of agents makes systems be more modifiable. We describe an agent-based software architecture that is intended to be easily extended and modified. This architecture is a redesign of a previous one using more formal principles and methods of software architecture design.

Applying virtual reality (VR) in combination with intelligent tutoring systems (ITSs) is a promising approach to computer based learning and training. However, the classical structure of ITSs has not been conceived to deal with the new sources of information and interaction provided by VR environments. The resulting structures combining both technologies often renounce to the traditional modular separation of ITSs in pursuit of increasing efficiency, but making very difficult the reusing of their components. In this paper it is described the extension of the classical ITS structure proposed in the MAEVIF model to build reusable and adaptable VR based ITSs.