Mean Field Theory in Doing Logic Programming Using Hopfield Network

Logic program and neural networks are two important perspectives in artificial intelligence. Logic describes connections among propositions. Moreover, logic must have descriptive symbolic tools to represent propositions. Meanwhile representation of neural networks on the other hand is in non-symbolic form. The objective in performing logic programming revolves around energy minimization is to reach the best global solutions. On the other hand, we usually gets local minima solutions also. In order to improve this, based on the Boltzmann machine concept, we will derive a learning algorithm in which time-consuming stochastic measurements of collerations are replaced by solutions to deterministic mean field theory (MFT) equations. The main idea of mean field algorithm is to replace the real unstable induced local field for each neuron in the network with its average local field value. Then, we build agent based modelling (ABM) by using Netlogo for this task.

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