Architectures

This section examines the various architectural paradigms that have been proposed and implemented in the pursuit of AGI. We move from pure symbolic systems to modern hybrid and neuro-symbolic models.

The Architectural Challenge

The central challenge in AGI is integration: how to combine perception, memory, reasoning, and learning into a single, cohesive system that can handle open-ended tasks in the real world.

Key Architectural Paradigms

1. Symbolic Architectures

Based on the Physical Symbol System Hypothesis, these architectures use explicit symbols and rules.

• Examples: Cyc, early SOAR.
• Focus: High-level reasoning and knowledge representation.

2. Connectionist (Neural) Architectures

Inspired by the biological brain, these use networks of simple processing units.

• Examples: Transformers, CNNs, RNNs.
• Focus: Perception, pattern recognition, and learning from raw data.

3. Evolutionary Architectures

Architectures that evolve their structure and parameters over time.

• Examples: NEAT, Genetic Programming, MOSES (OpenCog).
• Focus: Discovery of novel strategies and structures.

4. Bayesian/Probabilistic Architectures

Based on probabilistic graphical models and Bayesian inference.

• Focus: Reasoning under uncertainty and principled belief updating.

5. Neuro-symbolic Architectures

The "Best of Both Worlds" approach, attempting to bridge the gap between neural learning and symbolic reasoning.

• Focus: This is a major area of current AGI research, including the Hyperon framework.

Learning Path

1. Symbolic Systems: Understanding the power and limits of logic-based AI.
2. Neural Foundation: How modern deep learning provides the perceptual "front-end".
3. Neuro-symbolic Integration: Techniques for bridging symbols and neurons.
4. Hierarchical Systems: Modeling the brain's hierarchical structure.
5. Decentralized and Multi-Agent: Architectures for collective intelligence.

---

Next: Symbolic Architectures