# SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model

arXiv: https://arxiv.org/abs/1712.00929 · HTML: https://mlsanity.com/paper/1712.00929/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 15 of 15)

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- [The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain](https://mlsanity.com/paper/2103.06123/) (2021)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [Toward an Idiomatic Framework for Cognitive Robotics](https://mlsanity.com/paper/2111.13027/) (2021)
- [Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word](https://mlsanity.com/paper/2205.15027/) (2022)
- [Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information](https://mlsanity.com/paper/2103.06442/) (2021)
- [Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues](https://mlsanity.com/paper/2201.06786/) (2022)
- [Study of Emotion Concept Formation by Integrating Vision, Physiology, and Word Information using Multilayered Multimodal Latent Dirichlet Allocation](https://mlsanity.com/paper/2404.08295/) (2024)
- [Symbol emergence as interpersonal cross-situational learning: the emergence of lexical knowledge with combinatoriality](https://mlsanity.com/paper/2306.15837/) (2023)
- [Unsupervised Work Behavior Pattern Extraction Based on Hierarchical Probabilistic Model](https://mlsanity.com/paper/2405.09838/) (2024)
- [Social Comparison without Explicit Inference of Others' Reward Values: A Constructive Approach Using a Probabilistic Generative Model](https://mlsanity.com/paper/2512.18687/) (2025)

## Abstract (arXiv)

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed.