AI-Driven Insights: Revolutionizing Decision Making and Game Theory Applications
Abstract
Decision-making and game theory are crucial fields within artificial intelligence (AI), focusing on how agents can make rational choices in complex, interactive environments. AI techniques enable the modeling of decision-making processes and game theory, enhancing predictions of outcomes and optimal strategies. Game theory, a branch of mathematics, examines strategic interactions among multiple players. AI can simulate these games and analyze players' strategies to identify optimal moves across various domains, including economics, politics, military strategy, and sports. In decision-making, AI develops models that account for factors such as uncertainty, risk, and preferences, helping individuals and organizations make better choices while minimizing negative outcomes. Additionally, AI can automate decision-making in contexts like financial trading and medical diagnosis. By integrating game theory into decision-making, decision-makers can anticipate the actions of others and select strategies that maximize their objectives. This rigorous framework is valuable for analyzing scenarios like auctions, voting systems, and pricing strategies. As AI technology continues to advance, we can expect more sophisticated applications in these areas, leading to improved decision-making and more strategic outcomes. The potential of AI to revolutionize these fields lies in its ability to provide accurate models, swift computations, and better predictions of outcomes.
References
J. Chawla, A. K. Ahlawat, “Resolving Software Interoperability Issues of Unsigned Number and Date-Time Precision Using JADE Framework System,” International Journal of System of Systems Engineering, Inderscience, vol. 11, no. 3/4, pp. 380-398, March 2022.
S. Banala, “The Future of IT Operations: Harnessing Cloud Automation for Enhanced Efficiency and The Role of Generative AI Operational Excellence,” International Journal of Machine Learning and Artificial Intelligence, vol. 5, no. 5, pp. 1–15, Jul. 2024.
S. Banala, "DevOps Essentials: Key Practices for Continuous Integration and Continuous Delivery," International Numeric Journal of Machine Learning and Robots, vol. 8, no. 8, pp. 1-14, 2024.
M. R. M. Reethu, L. N. R. Mudunuri, and S. Banala, “Exploring the Big Five Personality Traits of Employees in Corporates,” FMDB Transactions on Sustainable Management Letters, vol. 2, no. 1, pp. 1–13, 2024.
S. Banala, “The Future of Site Reliability: Integrating Generative AI into SRE Practices,” FMDB Transactions on Sustainable Computer Letters, vol. 2, no. 1, pp. 14–25, 2024.
S. Banala, Identity and Access Management in the Cloud, International Journal of Innovations in Applied Sciences & Engineering, vol. 10, no. 1S, pp. 60–69, 2024.
B. Senapati and B. S. Rawal, "Adopting a deep learning split-protocol based predictive maintenance management system for industrial manufacturing operations," in Big Data Intelligence and Computing. DataCom 2022, C. Hsu, M. Xu, H. Cao, H. Baghban, and A. B. M. Shawkat Ali, Eds., Lecture Notes in Computer Science, vol. 13864. Singapore: Springer, 2023, pp. 25–38. doi: 10.1007/978-981-99-2233-8_2.
B. Senapati and B. S. Rawal, "Quantum communication with RLP quantum resistant cryptography in industrial manufacturing," Cyber Security and Applications, vol. 1, 2023, Art. no. 100019. doi: 10.1016/j.csa.2023.100019.
B. Senapati et al., "Wrist crack classification using deep learning and X-ray imaging," in Proceedings of the Second International Conference on Advances in Computing Research (ACR’24), K. Daimi and A. Al Sadoon, Eds., Lecture Notes in Networks and Systems, vol. 956. Cham: Springer, 2024, pp. 72–85. doi: 10.1007/978-3-031-56950-0_6
S. Banala, "The FinOps Framework: Integrating Finance and Operations in the Cloud," International Journal of Advances in Engineering Research, vol. 26, no. 6, pp. 11–23, 2024.
S. Banala, "Artificial Creativity and Pioneering Intelligence: Harnessing Generative AI to Transform Cloud Operations and Environments," International Journal of Innovations in Applied Sciences and Engineering, vol. 8, no. 1, pp. 34–40, 2023.
S. Banala, Cloud Sentry: Innovations in Advanced Threat Detection for Comprehensive Cloud Security Management, International Journal of Innovations in Scientific Engineering, vol. 17, no. 1, pp. 24–35, 2023.
S. Banala, Exploring the Cloudscape - A Comprehensive Roadmap for Transforming IT Infrastructure from On-Premises to Cloud-Based Solutions, International Journal of Universal Science and Engineering, vol. 8, no. 1, pp. 35–44, 2022.
A. Hamza and B. Kumar, "A Review Paper on DES AES RSA Encryption Standards," in 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 333-338, 2020.
S. Khan and S. Alqahtani, “Hybrid machine learning models to detect signs of depression,” Multimed. Tools Appl., vol. 83, no. 13, pp. 38819–38837, 2023.
S. Khan, “Artificial intelligence virtual assistants (chatbots) are innovative investigators,” Int. J. Comput. Sci. Netw. Secur., vol. 20, no. 2, pp. 93-98, 2020.
S. Khan, “Modern internet of things as a challenge for higher education,” Int. J. Comput. Sci. Netw. Secur., vol. 18, no. 12, pp. 34-41, 2018.
K. Sattar, T. Ahmad, H. M. Abdulghani, S. Khan, J. John, and S. A. Meo, “Social networking in medical schools: Medical student’s viewpoint,” Biomed Res., vol. 27, no. 4, pp. 1378-84, 2016.
S. Khan, “Study factors for student performance applying data mining regression model approach,” Int. J. Comput. Sci. Netw. Secur., vol. 21, no. 2, pp. 188-192, 2021.
S. Khan and A. Alfaifi, “Modeling of coronavirus behavior to predict its spread,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 5, pp. 394-399, 2020. doi: 10.14569/IJACSA.2020.0110552.
K. Singh, I. R. Khan, S. Khan, K. Pant, S. Debnath, and S. Miah, “Multichannel CNN model for biomedical entity reorganization,” Biomed Res. Int., vol. 2022, Art. ID 5765629, 2022.
M. S. Rao, S. Modi, R. Singh, K. L. Prasanna, S. Khan, and C. Ushapriya, “Integration of cloud computing, IoT, and big data for the development of a novel smart agriculture model,” presented at the 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023.
S. Khan et al., “Transformer architecture-based transfer learning for politeness prediction in conversation,” Sustainability, vol. 15, no. 14, p. 10828, 2023.
M. J. Antony, B. P. Sankaralingam, S. Khan, A. Almjally, N. A. Almujally, and R. K. Mahendran, “Brain–computer interface: The HOL–SSA decomposition and two-phase classification on the HGD EEG data,” Diagnostics, vol. 13, no. 17, p. 2852, 2023.
D. Dayana, T. S. Shanthi, G. Wali, P. V. Pramila, T. Sumitha, and M. Sudhakar, “Enhancing usability and control in artificial intelligence of things environments (AIoT) through semantic web control models,” in Semantic Web Technologies and Applications in Artificial Intelligence of Things, F. Ortiz-Rodriguez, A. Leyva-Mederos, S. Tiwari, A. Hernandez-Quintana, and J. Martinez-Rodriguez, Eds., IGI Global, USA, 2024, pp. 186–206, doi: 10.4018/979-8-3693-1487-6.ch009.
J. Tanwar, H. Sabrol, G. Wali, C. Bulla, R. K. Meenakshi, P. S. Tabeck, and B. Surjeet, “Integrating blockchain and deep learning for enhanced supply chain management in healthcare: A novel approach for Alzheimer’s and Parkinson’s disease prevention and control,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 22s, pp. 524–539, 2024.
R. K. Meenakshi, R. S., G. Wali, C. Bulla, J. Tanwar, M. Rao, and B. Surjeet, “AI integrated approach for enhancing linguistic natural language processing (NLP) models for multilingual sentiment analysis,” Philological Investigations, vol. 23, no. 1, pp. 233–247, 2024.
G. Wali and C. Bulla, “Suspicious activity detection model in bank transactions using deep learning with fog computing infrastructure,” in Advances in Computer Science Research, 2024, pp. 292–302, doi: 10.2991/978-94-6463-471-6_29.
G. Wali, P. Sivathapandi, C. Bulla, and P. B. Surjeet, “Early detection of Alzheimer’s disease and Parkinson’s disease using deep learning models in healthcare systems,” International Journal of Machine Learning Research, vol. 9, no. 2, pp. 168–178, 2024.