Representing a Decision Problem with a Decision Network
To represent a decision problem with a decision network, the primary step is to construct a graphical model capturing the relationship between random variables, decision variables, and utility functions. The decision network consists of nodes representing these elements and directed arcs depicting dependencies between them.
Arcs in the Graph
- Arcs to Decision Nodes: Represent information available when the decision is made.
- Arcs to Chance Nodes: Represent probabilistic dependence.
- Arcs to Utility Nodes: Represent what the utility depends on.
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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