How to Structure a Decision Network?
To structure a decision network, follow these key steps:
- Define Variables and Functions: Identify random variables, decision variables, and utility functions crucial for the decision problem.
- Node Representation: Represent random variables as chance nodes, decision variables as decision nodes, and utility functions as utility nodes.
- Connect Nodes: Use directed arcs to represent dependencies between variables.
- Directed Arcs:
- Arcs to decision nodes represent available information.
- Arcs to chance nodes represent probabilistic dependencies.
- Arcs to utility nodes represent utility dependencies.
- Ensure DAG Structure: Avoid cycles or feedback loops in the arcs to maintain a directed acyclic graph.
- Define Domains: Specify the domain for each random variable and decision variable. Utility nodes do not have domains.
- Conditional Probability Distributions: Provide conditional probability distributions for each random variable given their parents in the network.
- Utility Function: Define the utility function mapping the values of the variables it relies on to a real number representing the decision-maker’s preferences.
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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