To enrich this material, have shown advanced natural language processing we employed text generation techniques. We created capabilities across diverse tasks, making their a corpus where Queneau's style aligns with explainability an important area of research (Zhao Fénéon's unique style, and another corpus where et al., 2024). A key aspect of these models is their Fénéon's style varies in line with Queneau's plurality ability to generate meaningful text representations of styles. This design aimed to effectively through vector embeddings, that encode semantic assess the impact of topic and style on embedding information.

Although moral responsibility is not circumscribed by causality, they are both closely intermixed. Furthermore, rationally understanding the evolution of the physical world is inherently linked with the idea of causality. Thus, the decision-making applications based on automated planning inevitably have to deal with causality, especially if they consider imputability aspects or integrate references to ethical norms. The many debates around causation in the last decades have shown how complex this notion is and thus, how difficult is its integration with planning. As a result, much of the work in computational ethics relegates causality to the background, despite the considerations stated above. This paper's contribution is to provide a complete and sound translation into logic programming from an actual causation definition suitable for action languages, this definition is a formalisation of Wright's NESS test. The obtained logic program allows to deal with complex causal relations. In addition to enabling agents to reason about causality, this contribution specifically enables the computational ethics domain to handle situations that were previously out of reach. In a context where ethical considerations in decision-making are increasingly important, advances in computational ethics can greatly benefit the entire AI community.

This article compares Defeasible Description Logics (DDL) and Topological Approach to reason on Ontologies with exceptions. DDL is integration between Description Logics and Defeasible Logics to deal with monotonic and non-monotonic parts of the knowledge bases respectively. Topological approach tries to reason on inconsistent knowledge bases using the conventional topological operators e.g., interior, exterior, border and closure. We develop neo-Topology based on topological operators and we make major development and improvements of current Topological approach by properly introducing the ``Thickness Border'' with strong inference rules. We proof the validity of the inference rules using set operations. We demonstrate both approaches with appropriate example. We show the advantages and disadvantages of both approaches.