Utility Function

Utility functions mathematically represent preferences, guiding decision-making in diverse fields from economics to artificial intelligence. Current research focuses on learning utility functions from data, often employing machine learning techniques like reinforcement learning and neural networks (including generalized additive models) to model complex, potentially non-linear preferences, and addressing challenges like handling multiple objectives and uncertainty. This work is crucial for improving the design of AI agents, optimizing resource allocation in various systems (e.g., networks, hospitals), and gaining a deeper understanding of human decision-making processes.