Third edition of Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2023 is now available (including the full text).
6.6 Review
The following are the main points you should have learned from this chapter:
- Probability can be used to make decisions under uncertainty.
- The posterior probability is used to update an agent's beliefs based on evidence.
- A Bayesian belief network can be used to represent independence in a domain.
- Exact inference can be carried out for sparse graphs (with low treewidth).
- Stochastic simulation can be used for approximate inference.
- A hidden Markov model or a dynamic belief network can be used for probabilistic reasoning in time, such as for localization.
