Example of a Decision Network
Consider a simple medical diagnosis scenario where a doctor needs to decide whether to order a test for a patient based on the likelihood of a disease and the cost of the test. The decision network for this scenario might include:
- Chance Nodes: Disease presence (Yes/No), Test result (Positive/Negative)
- Decision Node: Order test (Yes/No)
- Utility Node: Overall patient health outcome and cost
The doctor can use the decision network to evaluate the expected utility of ordering the test versus not ordering it, taking into account the probabilities of disease presence and test results, and the utility values associated with different outcomes.
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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