Example of Representing a Decision Problem
Consider a simple decision network for deciding whether an agent should take an umbrella when going out. The agent’s utility depends on the weather and whether he takes an umbrella. The agent can only observe the forecast and does not observe the weather directly. The forecast depends on the weather.
Variables and Domains
- Weather: {norain, rain}
- Forecast: {sunny, rainy, cloudy}
- Umbrella: {take_it, leave_it}
Probabilities
- P(Weather = rain) = 0.3
- P(Forecast | Weather):
Weather | Forecast | Probability |
---|---|---|
norain | sunny | 0.7 |
norain | cloudy | 0.2 |
norain | rainy | 0.1 |
rain | sunny | 0.15 |
rain | cloudy | 0.25 |
rain | rainy | 0.6 |
Utility Function
- u(Weather, Umbrella):
Weather | Umbrella | Utility |
---|---|---|
norain | take_it | 20 |
norain | leave_it | 100 |
rain | take_it | 70 |
rain | leave_it | 0 |
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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