Learning mechanisms for adaptive agents
Learning mechanisms are key to enabling intelligent agents to adapt to changes in their environment or to improve over time. The ability to learn allows agents to continuously refine their knowledge and behavior based on new experiences and data. There are numerous approaches to learning, each with its own strengths and applications:
- Supervised learning: This learning paradigm involves training an agent on a dataset where the inputs are paired with corresponding labeled outputs or target values. The aim is for the agent to learn a mapping function that accurately predicts outputs for new unseen inputs. Supervised learning is widely used for classification and regression tasks across domains such as these:
- Image classification (for example, identifying objects and digits in images)
- Spam detection (classifying emails as spam or not spam)
- Machine translation (learning to map text in one language to another)
- Medical diagnosis (mapping patient...
