# A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI

arXiv: https://arxiv.org/abs/2401.06256 · HTML: https://mlsanity.com/paper/2401.06256/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 272 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Scene Graph Generation from Objects, Phrases and Region Captions](https://mlsanity.com/paper/1707.09700/) (2017)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [Detecting Visual Relationships with Deep Relational Networks](https://mlsanity.com/paper/1704.03114/) (2017)
- [Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation](https://mlsanity.com/paper/1806.11538/) (2018)
- [Video Generative Adversarial Networks: A Review](https://mlsanity.com/paper/2011.02250/) (2020)
- [Devil's on the Edges: Selective Quad Attention for Scene Graph Generation](https://mlsanity.com/paper/2304.03495/) (2023)
- [A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine](https://mlsanity.com/paper/2107.13704/) (2021)
- [Natural Language Guided Visual Relationship Detection](https://mlsanity.com/paper/1711.06032/) (2017)
- [Fifty Years of Prolog and Beyond](https://mlsanity.com/paper/2201.10816/) (2022)
- [End-to-End Entity Classification on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2003.12383/) (2020)
- [Knowledge Graph Extraction from Videos](https://mlsanity.com/paper/2007.10040/) (2020)
- [AI-Empowered Persuasive Video Generation: A Survey](https://mlsanity.com/paper/2112.09401/) (2021)
- [Multi-scale speaker embedding-based graph attention networks for speaker diarisation](https://mlsanity.com/paper/2110.03361/) (2021)
- [A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering](https://mlsanity.com/paper/2108.12820/) (2021)
- [Question Answering Over Biological Knowledge Graph via Amazon Alexa](https://mlsanity.com/paper/2210.06040/) (2022)
- [Cognitive Architecture for Co-Evolutionary Hybrid Intelligence](https://mlsanity.com/paper/2209.12623/) (2022)
- [Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning](https://mlsanity.com/paper/2303.13209/) (2023)
- [A Cognitive Architecture for the Implementation of Emotions in Computing Systems](https://mlsanity.com/paper/1606.02899/) (2016)
- [Concepts is All You Need: A More Direct Path to AGI](https://mlsanity.com/paper/2309.01622/) (2023)
- [Artwork creation by a cognitive architecture integrating computational creativity and dual process approaches](https://mlsanity.com/paper/1601.00669/) (2016)

## Built on by (most cited first, 1 of 1)

- [Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?](https://mlsanity.com/paper/2509.10875/) (2025)

## Abstract (arXiv)

The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a new cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph models are used, constructed as a development of annotated metagraphs. As components, the cognitive architecture being developed includes machine consciousness, machine subconsciousness, blocks of interaction with the external environment, a goal management block, an emotional control system, a block of social interaction, a block of reflection, an ethics block and a worldview block, a learning block, a monitoring block, blocks of statement and solving problems, self-organization and meta learning block.