Developing agent based modelling for doing logic programming in Hopfield network
Applied Mathematical Sciences
In recent studies on artificial intelligence, logic program occupies a significant position because of its attractive features. Neural networks are dynamic systems in the learning and training phase of their operation and convergence is an essential feature, so it is necessary for the researchers developing the models and their learning algorithms to find a provable criterion for convergence in a dynamic system. In this paper, an agent based modelling (ABM) was developed by using NETLOGO as a
... sing NETLOGO as a platform to carry out logic programming in Hopfield network. The developed model seems to illustrate the task of doing logic programming in a simpler and user friendly manner. Saratha Sathasivam and Ng Pei Fen Gadi Pinkas [2, 3] and Wan Abdullah [10, 11] defined a bi-directional mapping between propositional logic formulas and energy functions of symmetric neural networks. Both methods are interested in finding whether the solutions obtained are models a corresponding logic program. Both researchers interested with Hopfield network. Besides that, both approaches can handle non-monotonicity of logic. Pinkas introduced "preferred interpretation" in handling non-monotonicity. Meanwhile, Wan Abdullah's method hunts for the best solutions, given the clauses in the logic program, and the corresponding solutions may change as new clauses added. Even when clauses in the logic program are inconsistent, but yet by using Wan Abdullah's method, interpretation with least logical inconsistent can be obtained. Pinkas introduced "preferred interpretation" in handling nonmonotonicity. Meanwhile, Wan Abdullah's method hunts for the best solutions, given the clauses in the logic program, and the corresponding solutions may change as new clauses added. Even when clauses in the logic program are inconsistent, but yet by using Wan Abdullah's method, interpretation with least logical inconsistent can be obtained. However the main different between these both approaches are: Pinkas's work revolves around first-order logic meanwhile Wan Abdullah's work revolves around propositional Horn clauses and learning ability of the Hopfield network. Our objective is to find models for the corresponding logic program. In this paper, we develop agent based modelling (ABM) for doing logic programming in Hopfield network. Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. ABM is computer representation of systems that are comprised of multiple, interacting agents. We want to develop a user friendly approach to handle the task of logic programming. We limit our model to Horn clauses due to non-Horn clauses involve more computational complexity and triggered satisfiability problem.