A cognitive model of navigation and path finding using cellular automata agent

Artificial Intelligence constitute a continuum of attempts to model adaptive, learning and cognitive abilities in all the varying degrees of complexities we know from biology and psychology. The purpose of present research paper is to design cognitive cellular automata agent with conflict-level spatial problem-solving abilities. Such an agent will have the capability to reason, learn and plan in a manner similar to human being. The agent architecture has a fuzzy inference system to implement the “perceive-reason-act” decision cycle of a mobile cellular automata reflex agent. In essence the agent is expected to execute an Observe-Orient-Decide-Act (OODA) loop. A cognitive model is developed to compute the best-next-move at each time instant for the goal oriented, rational and utility-driven mobile cellular automata agent. Experiments are to be planned and conducted to evaluate the problem solving abilities of such an agent when immersed in a conflict situation.