Components of Decision Networks
A decision network consists of three types of nodes:
- Chance Nodes: Represent random variables and their possible values, capturing the uncertainty in the decision-making process.
- Decision Nodes: Represent the choices available to the decision-maker.
- Utility Nodes: Represent the utility or value of the outcomes, helping to evaluate and compare different decision paths.
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
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