Hybrid Artificial Intelligence Network in Taxation of Upheaval Damaged Structures

The knowledge base of the new ANN is reconstructed through the use of the Fuzzy Set Theory, which permits to formalization ion the procedures in a non-deterministic way. The uncertainty and the imprecision of the gathered data are managed with techniques topical to Fuzzy Logic. Moreover, the System permits the user to modify the same knowledge base or insert new basheadquartersd use the most appropriate one in specific situations. The gathered data and the evaluations' results are loaded on databases and databases, successively, for further elaborations and new evaluations with different knowledge bases. Implementing and fleshing out several psychological and neuroscience theories of cognition, the AI-ANN conceptual model aims at being a cognitive “theory of everything.” With modules or processes for perception, working memory, episodic memories, “consciousness,” procedural memory, action selection, perceptual learning, serial learning, deliberation, volition, and non-routine problem solving, the AI-ANN model is ideally suited to provide a functional ontology that would allow for the discussion, design, and comparison of AGI systems. The AI-ANN architecture is based on the cognitive cycle, a “cognitive atom.” The more elementary cognitive modules and processes play a role in each mental cycle. Higher-level functions are performed over multiple cycles. In addition to giving a quick overview of the AI-ANN conceptual model and its underlying computational technology, one can argue for the AI-ANN architecture’s role as a foundational architecture for an AGI. Finally, lessons For AGI researchers drawn from the model and its architecture are discussed.