# The General Theory of General Intelligence: A Pragmatic Patternist Perspective

arXiv: https://arxiv.org/abs/2103.15100 · HTML: https://mlsanity.com/paper/2103.15100/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 87 references that are in the map.
- First posted on arXiv 28 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [The Consciousness Prior](https://mlsanity.com/paper/1709.08568/) (2017)
- [An Introduction to Probabilistic Programming](https://mlsanity.com/paper/1809.10756/) (2018)
- [The AGI Containment Problem](https://mlsanity.com/paper/1604.00545/) (2016)
- [First Neural Conjecturing Datasets and Experiments](https://mlsanity.com/paper/2005.14664/) (2020)
- [Toward a Formal Model of Cognitive Synergy](https://mlsanity.com/paper/1703.04361/) (2017)
- [Open Ended Intelligence: The individuation of Intelligent Agents](https://mlsanity.com/paper/1505.06366/) (2015)
- [Folding and Unfolding on Metagraphs](https://mlsanity.com/paper/2012.01759/) (2020)
- [Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation](https://mlsanity.com/paper/2012.14474/) (2020)
- [Symbol Grounding via Chaining of Morphisms](https://mlsanity.com/paper/1703.04368/) (2017)
- [Grounding Occam's Razor in a Formal Theory of Simplicity](https://mlsanity.com/paper/2004.05269/) (2020)
- [Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation](https://mlsanity.com/paper/1902.00741/) (2019)
- [A theory of incremental compression](https://mlsanity.com/paper/1908.03781/) (2019)
- [Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities](https://mlsanity.com/paper/2005.12533/) (2020)
- [Patterns of Cognition: Cognitive Algorithms as Galois Connections Fulfilled by Chronomorphisms On Probabilistically Typed Metagraphs](https://mlsanity.com/paper/2102.10581/) (2021)
- [Artificial general intelligence through recursive data compression and grounded reasoning: a position paper](https://mlsanity.com/paper/1506.04366/) (2015)
- [Combinatorial Decision Dags: A Natural Computational Model for General Intelligence](https://mlsanity.com/paper/2004.05268/) (2020)
- [Maximal Algorithmic Caliber and Algorithmic Causal Network Inference: General Principles of Real-World General Intelligence?](https://mlsanity.com/paper/2005.04589/) (2020)

## Built on by (most cited first, 8 of 8)

- [OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond](https://mlsanity.com/paper/2310.18318/) (2023)
- [Agent Identity Evals: Measuring Agentic Identity](https://mlsanity.com/paper/2507.17257/) (2025)
- [Quantum AIXI: Universal Intelligence via Quantum Information](https://mlsanity.com/paper/2505.21170/) (2025)
- [What the F*ck Is Artificial General Intelligence?](https://mlsanity.com/paper/2503.23923/) (2025)
- [Hamiltonian Formalism for Comparing Quantum and Classical Intelligence](https://mlsanity.com/paper/2506.14456/) (2025)
- [Computational Concept of the Psyche (in Russian)](https://mlsanity.com/paper/2509.07009/) (2025)
- [Creativity in AI as Emergence from Domain-Limited Generative Models](https://mlsanity.com/paper/2601.08388/) (2026)
- [Computational Concept of the Psyche](https://mlsanity.com/paper/2603.15586/) (2026)

## Abstract (arXiv)

A multi-decade exploration into the theoretical foundations of artificial and natural general intelligence, which has been expressed in a series of books and papers and used to guide a series of practical and research-prototype software systems, is reviewed at a moderate level of detail. The review covers underlying philosophies (patternist philosophy of mind, foundational phenomenological and logical ontology), formalizations of the concept of intelligence, and a proposed high level architecture for AGI systems partly driven by these formalizations and philosophies. The implementation of specific cognitive processes such as logical reasoning, program learning, clustering and attention allocation in the context and language of this high level architecture is considered, as is the importance of a common (e.g. typed metagraph based) knowledge representation for enabling "cognitive synergy" between the various processes. The specifics of human-like cognitive architecture are presented as manifestations of these general principles, and key aspects of machine consciousness and machine ethics are also treated in this context. Lessons for practical implementation of advanced AGI in frameworks such as OpenCog Hyperon are briefly considered.