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A Step from Probabilistic Programming to Cognitive Architectures

Authors:

Year: 2016
Venue: arXiv:1605.01180
Links: arXiv abstract

Summary

Argues that probabilistic programming can represent the basic components of cognitive architectures in a unified, elegant fashion, while ideas from cognitive architectures — implicit specification of generative models via declared concepts and links, and the use of declarative knowledge for efficient inference — can in turn extend probabilistic-programming languages.

Relevance to Hyperon

A bridge source connecting probabilistic programming to cognitive-architecture design — adjacent to PLN's role as the uncertain-reasoning component of an integrative architecture and to the broader probabilistic-inference foundations referenced in the PLN Deep Dive.

Key References

• RawData source
• PLN Deep Dive