Decision based Cognitive Learning using Strategic Game Theory

Abstract

Objective: To modernize a system that can bring its own decision independently. Methodology: In this paper, we are projecting a novel model of the cognitive learning process using similar to the human learning technique. Findings: We have proved here that the relationship between two individuals may change their judgment in the same surroundings. Many researchers and scientists are working together over than six decades to develop an intelligence system as a human. Decision making is not so elementary. Each and every decision depends upon prior knowledge and decisions. With a slight change in nature may change the decision from pros to cons, from good to bad. In such a dynamic environment, we need to develop some dynamic system that can change the decision accordingly to the environment. Game theory plays an important role to handle such dynamic decision making in this world. We make decisions and learn through the observations and experience and then store the observed or concluded results into our knowledge base. Learning makes us more powerful to produce a sound determination. Rule based systems define the relationship between a person to another, then that decision does efficiently and consequently to the kinship. Application: This paper introduces a new version of thinking capability of machine in dynamic nature using game theory. We trust that this paper will require a revolution in sound system design and clay sculpture.