We propose a model of working memory based on the object file theory in cognitive science. This model is expressed as a generative model using a Bayesian network. We implemented a prototype system of cognitive architecture with this working memory, and as a result, we expected that the description of behavior rules would be concise and versatile.

In the first half of this paper, I discuss concepts surrounding the term "fluid intelli- gence," which often appears in general intelligence literature. In the second half, I discuss tasks for testing working memory, which is considered to be an essential cognitive function for the realization of fluid intelligence, and propose to use a sample-matching task to start with.

In [1] we discussed the theory of artificial general intelligence. There we established the foundation of mathematics. Here after establishing quantum Yang-Mills theory we discuss time paradox and time machine. Through the theory we may use our artificial general intelligence and develop discussion on good data by way of what we call the role playing of bullying and positive spiral.