Memory as a Mass-based Graph: Towards a Conceptual Framework for the Simulation Model of Human Memory in AI
This work addresses the problem of creating more cognitively plausible memory models for AI researchers, though it appears incremental as it builds on existing cognitive approaches without demonstrating broad empirical validation.
The paper tackles the challenge of simulating human memory in AI by proposing a mass-based graph model that distinguishes between core and peripheral memories, translating topographical differences into node quantities to represent the importance of atomic propositions for an intelligent being.
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.