Sustainable Command and Control Indexing Based on Artificial Intelligence

The functional nature, operational environment, expertise, weapons and diverse strategies of the military forces, along with the diverse actions and threats of the hostile forces, have highlighted the requirement of integrated and network-oriented sustainable command and control systems. These requirements have revolutionized the development of a stable and secure infrastructure for continuous and complementary command unity in the battlefield. The extent, complexity and increasing progress of command and control technologies require the use of tools such as artificial intelligence to increase the stability component in these systems. The purpose of this research is to index sustainable command and control based on the essential capabilities of artificial intelligence. The type of applied research is developmental with a mixed approach. The research method is quantitative content analysis through library and documentary information collection tools. Network analysis (ANP) has been used to analyze the information, and Super Decision software has been used to identify the network relationships of the essential capabilities of artificial intelligence in relation to sustainability. Based on the results of the research, the main indicators of intelligence include data processing, knowing the environment, knowing oneself, simulation, quick reaction, forecasting, problem solving, planning, learning, understanding distinction and similarity, and decision-making, on the stability indicators of command and control, including lack of identification. Non-hitting and non-destruction have had the most impact.