Synergies between Machine Learning, Artificial Intelligence, and Game Theory for Complex Decision-Making

Abstract

The special focus of this paper is to discuss a likely intersection of machine learning, artificial intelligence (AI) technology, and game theory, pointing at the importance of this synthesis both in mathematics and engineering. As these domains develop various means of addressing decision-making problems become more and more sophisticated and can be used in different areas such as economics, security, and social sciences. We will also address selected game-theoretic issues including the concept of decision making in terms of Nash equilibria or in the distinction of games as being cooperative or non-cooperative and how they work in synergy with machine learning approaches bringing in reinforcements and deep learning to leverage forecasting and strategizing. The paper makes references to problem-oriented branches of studies such as autonomous systems or market strategies stressing the importance of the novel direction for further studies. Within the scope of machine learning and game theory the goal is to implement better complex models which utilize real world intricacies for enhancing decision making within a populated agent environment.