Structure of Decision Networks
The structure of a decision network is typically represented as a directed acyclic graph (DAG), where:
- Arcs (Edges): Indicate relationships between nodes. Arcs pointing to chance nodes signify dependencies between random variables, arcs pointing to decision nodes signify information available at the time of decision, and arcs pointing to utility nodes represent factors influencing the utility.
Decision Networks in AI
Decision networks, also known as influence diagrams, play a crucial role in artificial intelligence by providing a structured framework for making decisions under uncertainty. These graphical representations integrate decision theory and probability, enabling AI systems to systematically evaluate various actions and their potential outcomes. In this article, we will explore the components, structure, and applications of decision networks in AI.
Table of Content
- What is a Decision Network?
- Components of Decision Networks
- Example of a Decision Network
- Structure of Decision Networks
- Representing a Decision Problem with a Decision Network
- How to Structure a Decision Network?
- Example of Representing a Decision Problem
- Maximum Expected Utility
- No-Forgetting Agent and Decision Network
- Evaluating Decision Networks
- Applications of Decision Networks in AI
- Advantages of Decision Networks
- Conclusion
Contact Us