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A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots by Taniguchi, Tadahiro; Yamakawa, Hiroshi; Nagai, Takayuki; Doya, Kenji; Sakagami, Masamichi; Suzuki, Masahiro; Nakamura, Tomoaki; Taniguchi, Akira is a scholarly article available to read on EtoBox.

What is A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots about?

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs. The development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information. In this study, we describe the rationale of WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modu

Author

Taniguchi, Tadahiro; Yamakawa, Hiroshi; Nagai, Takayuki; Doya, Kenji; Sakagami, Masamichi; Suzuki, Masahiro; Nakamura, Tomoaki; Taniguchi, Akira

Published

2021

Language

EN