This paper presents a succinct review of attempts in the literature to use game theory to model decision-making scenarios relevant to defence applications. Game theory has been proven as a very effective tool in modelling the decision-making processes of intelligent agents, entities, and players. It has been used to model scenarios from diverse fields such as economics, evolutionary biology, and computer science. In defence applications, there is often a need to model and predict the actions of hostile actors, and players who try to evade or out-smart each other. Modelling how the actions of competitive players shape the decision making of each other is the forte of game theory. In past decades, there have been several studies that applied different branches of game theory to model a range of defence-related scenarios. This paper provides a structured review of such attempts, and classifies existing literature in terms of the kind of warfare modelled, the types of games used, and the players involved. After careful selection, a total of 29 directly relevant papers are discussed and classified. In terms of the warfares modelled, we recognise that most papers that apply game theory in defence settings are concerned with Command and Control Warfare, and can be further classified into papers dealing with (i) Resource Allocation Warfare (ii) Information Warfare (iii) Weapons Control Warfare, and (iv) Adversary Monitoring Warfare. We also observe that most of the reviewed papers are concerned with sensing, tracking, and large sensor networks, and the studied problems have parallels in sensor network analysis in the civilian domain. In terms of the games used, we classify the reviewed papers into papers that use non-cooperative or cooperative games, simultaneous or sequential games, discrete or continuous games, and non-zero-sum or zero-sum games. Similarly, papers are also classified into two-player, three-player or multi-player game based papers. We also explore the nature of players and the construction of payoff functions in each scenario. Finally, we also identify gaps in literature where game theory could be fruitfully applied in scenarios hitherto unexplored using game theory. The presented analysis provides a concise summary of the state-of-the-art with regards to the use of game theory in defence applications and highlights the benefits and limitations of game theory in the considered scenarios.
Missile guidance systems using the Proportional Navigation (PN) guidance law is limited in performance in supporting wide class of engagement scenarios with varying mission and target parameters. For surpassing this limitation, an Artificial Neural Network (ANN) to substitute the PN guidance is proposed by the authors. The ANN based system enables learning, adaptation, and faster throughput and thus equips the guidance system with capability akin to intelligent biological organisms. This improvement could remove the barrier of limitations with allowable mission scope. In this paper, a Multi-Layer Perceptron (MLP) has been selected to implement the ANN based approach for replacing PN guidance. Attempts to replace PN guidance using MLP are limited in the literature and warrant greater attention due to significant theoretical development with the MLP field in recent times. It is shown in this paper, that the MLP based guidance law can effectively substitute PN for a wide range of engagement scenarios with variations in initial conditions. A foundational argument to justify using an MLP for substituting PN is provided. Besides this, the design, training and simulation based testing approach for an MLP to replace PN has been devised and described. The potential for faster throughput is possible as the MLP nodes process information in parallel when generating PN like guidance commands. The results clearly demonstrate the potential of MLP in future applications to effectively replace and thus upgrade a wide spectrum of modern missile guidance laws.
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