Introduction
- Probabilistic reasoning is a fundamental concept in AI that deals with uncertainty and incomplete information.
- It’s used in various AI applications, such as medical diagnosis, weather forecasting, and spam filtering.
Uncertainty in AI
- Uncertainty arises in AI due to several factors, including noisy sensor readings, incomplete knowledge, and nondeterministic environments.
- Probability theory provides a framework for representing and reasoning with uncertainty.
Probability Basics
- Probability is a measure of the likelihood of an event occurring.
- Key concepts include:
- Prior probability: The initial probability of an event.
- Conditional probability: The probability of an event given some evidence.
- Joint probability: The probability of two or more events occurring together.
Probabilistic Inference
- Probabilistic inference is the process of deriving new knowledge from uncertain information using probability theory.
- Various techniques exist for probabilistic inference, including:
- Bayes’ Theorem (covered in the next lecture)
- Bayesian networks
- Markov models
Applications of Probabilistic Reasoning
- Probabilistic reasoning is used in various AI applications:
- Medical diagnosis
- Weather forecasting
- Spam filtering
- Machine learning
Conclusion
- Probabilistic reasoning is a powerful tool for dealing with uncertainty in AI.
- It allows AI systems to make informed decisions even when faced with incomplete information.
References:
- Russell, S., and Norvig, P. Artificial Intelligence: A Modern Approach, 4th Edition, 2020, Pearson.
- Rich, E., Knight, K., & Nair, S. B. Artificial Intelligence. McGraw-Hill International.
- Nilsson, N. J. Artificial Intelligence: A New Synthesis. Morgan Kaufmann.
Note: This content was generated with the assistance of Google’s Gemini AI.