Conceptual structures

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Computer scientists create models of perceived reality to emulate cognitive behavior, such as reasoning and learning, which are central to AI research. These models arise from various acquisition and inference mechanisms, including perception, concept learning, conceptual clustering, hypothesis testing, and probabilistic inference. They are represented through different paradigms closely linked to their processes. Key paradigms include biological models (neural networks, genetic programming), logic-based models (first-order logic, modal logic, rule-based systems), virtual reality models (object and agent systems), probabilistic models (Bayesian networks, fuzzy logic), and linguistic models (conceptual dependency graphs, language-based representations). One of the strengths of Conceptual Graph (CG) theory is its versatility across these representation paradigms, making it a popular choice for numerous applications. Its integration with various cognitive processes fosters collaboration among research communities, such as Description Logic, Formal Concept Analysis, and Computational Linguistics. Increasingly, research outcomes from one community enhance the others, establishing a shared philosophical foundation for successful synergy.