Abstract from the paper:

The need for Commonsense Knowledge in the machine becomes more and more apparent, as we try to move forward in the development of Artificial (General) Intelligence. It is becoming evident that this kind of knowledge is paramount in the human cognitive capacity and therefore also crucial for machine intelligence to ever reach any level of performance nearing that of a human brain. However, attaining a sufficient amount, and qualitative level, of human Commonsense Knowledge in the machine, appears to be an ‘AI­hard’ or ‘AI­complete’ problem.

How do humans do this? There is a lot to be learned from child development, and although there are AI­projects that try to use a developmental model to ‘grow’ intelligence, there have not been any (relevant) Commonsense Knowledge projects that leveraged the child development paradigm. That is, until now.

I present ASTRID (Analysis of Systemic Tagging Results in Intelligent Dynamics), a real­world implementation of a Cognitive Architecture based on human developmental models. The current state of this project is the result of a full decade of research and development. This paper describes the project background, underlying philosophies, objectives and current results and insights.

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