The design approach for creating infra-sentient conversational agents in Project AllMind is grounded in advanced principles of cognitive science, computational psychology, and computational cognition, drawing on the theoretical frameworks that define human thought, memory, and communication. The development of these conversational agents is inspired by the cognitive processes underlying human intelligence, where memory, context, and learning play pivotal roles in shaping our interactions and understanding of the world. Cognitive models such as context-aware neural networks and probabilistic reasoning frameworks have demonstrated the ability to emulate key aspects of human cognition, such as maintaining conversational context, adapting to new information, and reflecting on past knowledge to generate novel insights
(Tenenbaum et al., 2011;
Sutton & Barto, 2018). By leveraging these models, the methodology ensures that each conversational agent not only mimics the intellectual styles and reasoning patterns of historical figures but also engages in meaningful, contextually relevant dialogue, thereby providing users with an authentic and immersive intellectual experience. This approach is underpinned by a rigorous mathematical framework that integrates elements of Bayesian inference, reinforcement learning, and topic modelling, reflecting the multi-faceted nature of human cognition and enabling the conversational agents to operate with a degree of nuance and depth comparable to human interlocutors.