Enabling AI-Based Smart Contracts on Blockchain: Automating Complex Decision-Making Processes
Keywords:
Artificial Intelligence, Smart ContractsAbstract
The convergence of Artificial Intelligence (AI) and blockchain technology is set to revolutionize various industries by enabling automated and intelligent decision-making processes through smart contracts. This paper explores the integration of AI into blockchain-based smart contracts, focusing on how this synergy can facilitate the automatic execution of complex decision-making processes in sectors such as insurance, real estate, and finance. By leveraging machine learning algorithms and natural language processing (NLP), AI enhances the capabilities of smart contracts, allowing them to process data, assess conditions, and execute transactions without human intervention. The paper discusses the benefits, challenges, and potential applications of AI-enabled smart contracts, as well as case studies that highlight their effectiveness in real-world scenarios. Furthermore, it examines the ethical implications and regulatory considerations that arise from this integration. Ultimately, this study aims to provide insights into how AI-based smart contracts can enhance operational efficiency and innovation across diverse industries.
Downloads
References
Gayam, Swaroop Reddy. "Deep Learning for Autonomous Driving: Techniques for Object Detection, Path Planning, and Safety Assurance in Self-Driving Cars." Journal of AI in Healthcare and Medicine 2.1 (2022): 170-200.
Chitta, Subrahmanyasarma, et al. "Decentralized Finance (DeFi): A Comprehensive Study of Protocols and Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 124-145.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Real-Time Logistics and Transportation Optimization in Retail Supply Chains: Techniques, Models, and Applications." Journal of Machine Learning for Healthcare Decision Support 1.1 (2021): 88-126.
Putha, Sudharshan. "AI-Driven Predictive Analytics for Supply Chain Optimization in the Automotive Industry." Journal of Science & Technology 3.1 (2022): 39-80.
Sahu, Mohit Kumar. "Advanced AI Techniques for Optimizing Inventory Management and Demand Forecasting in Retail Supply Chains." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 190-224.
Kasaraneni, Bhavani Prasad. "AI-Driven Solutions for Enhancing Customer Engagement in Auto Insurance: Techniques, Models, and Best Practices." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 344-376.
Vangoor, Vinay Kumar Reddy, et al. "Energy-Efficient Consensus Mechanisms for Sustainable Blockchain Networks." Journal of Science & Technology 1.1 (2020): 488-510.
Kondapaka, Krishna Kanth. "AI-Driven Inventory Optimization in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 377-409.
Kasaraneni, Ramana Kumar. "AI-Enhanced Supply Chain Collaboration Platforms for Retail: Improving Coordination and Reducing Costs." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 410-450.
Pattyam, Sandeep Pushyamitra. "Artificial Intelligence for Healthcare Diagnostics: Techniques for Disease Prediction, Personalized Treatment, and Patient Monitoring." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 309-343.
Kuna, Siva Sarana. "Utilizing Machine Learning for Dynamic Pricing Models in Insurance." Journal of Machine Learning in Pharmaceutical Research 4.1 (2024): 186-232.
George, Jabin Geevarghese. "Augmenting Enterprise Systems and Financial Processes for transforming Architecture for a Major Genomics Industry Leader." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 242-285.
Katari, Pranadeep, et al. "Cross-Chain Asset Transfer: Implementing Atomic Swaps for Blockchain Interoperability." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 102-123.
Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.
Venkata, Ashok Kumar Pamidi, et al. "Implementing Privacy-Preserving Blockchain Transactions using Zero-Knowledge Proofs." Blockchain Technology and Distributed Systems 3.1 (2023): 21-42.
Namperumal, Gunaseelan, Debasish Paul, and Rajalakshmi Soundarapandiyan. "Deploying LLMs for Insurance Underwriting and Claims Processing: A Comprehensive Guide to Training, Model Validation, and Regulatory Compliance." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 226-263.
Yellepeddi, Sai Manoj, et al. "Blockchain Interoperability: Bridging Different Distributed Ledger Technologies." Blockchain Technology and Distributed Systems 2.1 (2022): 108-129.
D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.
