Neuro Symbolic Integration and Agent Based Modelling

• Main Authors: Sathasivam , Saratha, Velavan, Muraly
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: QA1-939 Mathematics
• Online Access: http://eprints.usm.my/41097/1/saratha_icm_2018.pdf; http://eprints.usm.my/41097/; https://www.imrfedu.org/icm2018

Logic program and neural networks are two important perspectives in artificial intelligence. The major domain of neuro-symbolic integration is designed by the theory are usually known as deductive systems which less such elements of human reasoning as adaptation, learning and self-organisation. Meanwhile, neural networks, known as a mathematical model of neurons in the human brain, and have various abilities, and moreover, they also provide parallel computations and therefore can perform some calculations quicker than classical learning algorithms. Hopfield network is a feedback (recurrent) neural network, consisting of a set of N interconnected neurons which each neurons are linked to all others in all the directions. It has synaptic strength pattern which involve Lyapunov function E (energy function) for energy minimization events. It operates as content addressable memory systems with binary or bipolar threshold units