The pursuit of artificial general intelligence necessitates a departure from single-paradigm models and a move towards a sophisticated cognitive architecture that mirrors the complexity of human thought. Such an engine can be conceptualized as a multi-layered system, where information is not merely processed linearly but is subjected to a comprehensive analysis through an array of interconnected modules. At its core, this architecture is a Semantic Deep Belief Network (SDBN), augmented with Bayesian and commonsense reasoning to transcend simple pattern recognition and achieve a form of robust, agentic cognition.
The process begins in the agent thought store, a massive, unstructured repository of data ranging from raw text and images to structured sensor readings and historical interactions. When a query or stimulus is received, the system does not simply look up a direct answer. Instead, it processes this initial input as a thought chain, a series of activated concepts and associations drawn from the store. This nascent thought chain is then fed into a central centrifuge reactor, a metaphorical core where parallel processing and analysis occur. Within this reactor, a dynamic set of components works in concert. The SDBN deciphers the semantic meaning and hierarchical relationships within the thought chain, while a Bayesian network applies probabilistic reasoning to infer the most likely connections and causal relationships, providing a foundation for commonsense understanding. Simultaneously, an argumentation framework evaluates the logical consistency and coherence of the data, and agentic reasoners apply goal-oriented rules to guide the analysis toward a specific objective. A self-organizing map (SOM) clusters the data points, revealing latent, non-obvious relationships that might escape traditional analysis, enriching the overall context.
The output from this multi-modal analysis is then reduced and refined. This high-dimensional, processed information is funneled into another set of belief networks, which distill the complex findings into a concise set of updated beliefs. This is a critical step, as it prevents cognitive overload and ensures efficiency. Autoencoders further compress this data, retaining only the most essential features for future use. The final, distilled knowledge is then passed to a suite of deep learning-based reinforcement models. These models learn to act on the refined information, whether it's generating a coherent response, making a decision, or solving a problem.
The entire process is supported by a dual-memory system. A short-term memory (STM) holds the active thought chain and the intermediate outputs from the centrifuge for immediate use, allowing for rapid, in-the-moment decision-making. Concurrently, a long-term memory (LTM) integrates the final, compressed knowledge, updating the agent's overall world model and enriching the thought store for future queries. This continuous learning loop ensures that every interaction not only contributes to the current output but also enhances the system's foundational intelligence, making it more knowledgeable and efficient over time.