Uncertainty and Decision Making Situations Quiz

Uncertainty in Artificial Intelligence

• Uncertainty arises in various situations, such as traveling, insurance policy, brain estimation, game playing, and medical diagnoses.
• Systems that can reason about uncertainty should perform better than those that don't.

Representing Uncertainty

• Two toy examples: toothache diagnosis and burglar alarm scenario.
• In these examples, uncertainty can be quantified, and probabilistic reasoning can be used to calculate the likelihood of a hypothesized cause.

Causes of Uncertainty

• Information from unreliable sources.
• Experimental errors.
• Equipment fault.
• Temperature variation.
• Climate change.

Probabilistic Reasoning

• A way of knowledge representation that applies probability to indicate uncertainty in knowledge.
• Combines probability theory with logic to handle uncertainty.
• Uses probability to handle uncertainty due to laziness and ignorance.

Need for Probabilistic Reasoning in AI

• When there are unpredictable outcomes.
• When specifications or possibilities of predicates become too large to handle.
• When an unknown error occurs during an experiment.

Solving Problems with Uncertain Knowledge

• Two ways to solve problems: Bayes' rule and Bayesian statistics.

Importance of Reasoning Under Uncertainty

• The human brain uses Bayes' rule to combine uncertain information.
• Reasoning under uncertainty is an important area of AI, and statistical methods, especially Bayesian reasoning, have become a cornerstone of modern AI.

Overview of Artificial Intelligence

• Artificial Intelligence is the study of building agents that act rationally.
• It includes subsets such as search algorithms, games, and problem solving.