Game Theory Applications in AI
The convergence of artificial intelligence (AI) and game theory presents intriguing research and application opportunities. This overview explores the fundamental concepts of game theory and its influence on AI systems and techniques. Various AI applications of game theory are examined, including mechanism design, auctions, adversarial machine learning, reinforcement learning, and multi-agent systems. Additionally, we delve into game-theoretic methods utilized in AI, such as cooperative game theory, Nash equilibria computation, and the minimax algorithm. Through case studies and comparative analysis, we assess the strengths, weaknesses, current challenges, and future directions in this field. The aim of this review is to illuminate the synergies between game theory and artificial intelligence.
Authors
- Umesh C. Sharma (ORCID: https://orcid.org/0000-0003-0476-2523)
- Rachna Rathore (ORCID: https://orcid.org/0000-0001-8753-888X)
- Prerna Sharma (ORCID: https://orcid.org/0000-0002-1033-1845)
- Praveen Kumar Sahu
- Abhinash Jenasamanta
- S. Shilpa
Institutions
- Jiwaji University (IN)
- Lingaya's Vidyapeeth (IN)
- Mangalayatan University (IN)
- Dhanalakshmi Srinivasan Group of Institutions (IN)
- Usha Martin (India) (IN)
Publication Details
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1109/aece62803.2024.10911263
- Primary Topic
- Computability, Logic, AI Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00