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A knowledge base can be thought of as representing the beliefs of such an agent. Like a child, a strong-AI (AGI) robot would have to learn through input and experiences, constantly progressing and advancing its abilities over time. Both with statistical AI generated by neural networks we need also the concept of \\textsl{causality} of events traduced into directionality of logic entailments and deductions in order to give to robots the emulation of human intelligence. Moreover, by using the axioms we can guarantee the \\textsl{controlled security} about robot's actions based on logic inferences.\n  For AGI robots we consider the 4-valued Belnap's bilattice of truth-values with knowledge ordering as well, where the value \"unknown\" is the bottom value, the sent","title":"Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions","url":"https://arxiv.org/abs/2604.09567","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.09567v2 Announce Type: replace-cross \nAbstract: Knowledge representation formalisms are aimed to represent general conceptual information and are typically used in the construction of the knowledge base of reasoning agent. A knowledge base can be thought of as representing the beliefs of such an agent. Like a child, a strong-AI (AGI) robot would have to learn through input and experiences, constantly progressing and advancing its abilities over time. Both with statistical AI generated by neural networks we need also the concept of \\textsl{causality} of events traduced into directionality of logic entailments and deductions in order to give to robots the emulation of human intelligence. 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