Generative AI

🩺 Model-based Reflex Agents

These agents use an internal representation of the world to assess current conditions and predict potential outcomes before making a decision. Instead of reacting purely based on immediate inputs, they rely on stored knowledge to evaluate different possibilities, making them useful in healthcare for diagnostics, treatment recommendations, and patient monitoring.

💰 Goal-based Agents

Goal-based agents make decisions by evaluating multiple strategies to determine the most efficient way to reach a specific objective. They do not just react to inputs but plan a series of actions that maximize success. In finance, they can be used for investment portfolio optimization, fraud detection.

📦 Utility-based Agents

Utility-based agents go beyond achieving a goal; they evaluate multiple scenarios and select the one that provides the highest overall benefit based on specific utility measures. In supply chain management, these agents analyze factors such as cost efficiency, delivery time, and inventory levels to optimize logistics, reduce waste, and improve customer satisfaction through smarter decision-making.

🛡️ Learning Agents

Learning agents continuously improve their performance by interacting with their environment and refining their decision-making process based on experience and feedback. They use machine learning techniques to detect patterns, adapt to changing conditions, and optimize results over time.

🏡 Hierarchical Agents

Hierarchical agents are structured in multiple layers, where high-level agents break down complex tasks into smaller sub-tasks, which are then managed by lower-level agents. This allows them to handle large-scale operations efficiently. In real estate, these agents can manage property evaluation, price prediction, and automated negotiations.