Memory as a Mass-based Graph: Towards a Conceptual Framework for the Simulation Model of Human Memory in AI. (arXiv:2305.19274v1 [cs.AI])

There are two approaches for simulating memory as well as learning in
artificial intelligence; the functionalistic approach and the cognitive
approach. The necessary condition to put the second approach into account is to
provide a model of brain activity that contains a quite good congruence with
observational facts such as mistakes and forgotten experiences. Given that
human memory has a solid core that includes the components of our identity, our
family and our hometown, the major and determinative events of our lives, and
the countless repeated and accepted facts of our culture, the more we go to the
peripheral spots the data becomes flimsier and more easily exposed to oblivion.
It was essential to propose a model in which the topographical differences are
quite distinguishable. In our proposed model, we have translated this
topographical situation into quantities, which are attributed to the nodes. The
result is an edge-weighted graph with mass-based values on the nodes which
demonstrates the importance of each atomic proposition, as a truth, for an
intelligent being. Furthermore, it dynamically develops and modifies, and in
successive phases, it changes the mass of the nodes and weight of the edges
depending on gathered inputs from the environment.