Monotonic Systems in AI

What is a monotonic system in AI?

• A system where properties do not change with new information.

• A system that retracts previous conclusions.

• A system that only works with numerical data.

• A system that is always correct.

What is the objective of using a monotonic system in medical diagnostics?

• To create a system that retracts conclusions.

• To ensure consistent and reliable diagnoses.

• To allow for flexible reasoning.

• To simplify the diagnostic process.

What is the inference mechanism used in the diagnostic AI?

• Forward-chaining inference model.

• Backward-chaining inference model.

• Random inference model.

• Static inference model.

What is a potential challenge of monotonic systems?

• They can lead to overly broad diagnoses.

• They are always accurate.

• They require less data.

• They are easy to implement.

What is a key takeaway regarding the strength of monotonic systems?

• They ensure stable conclusions.

• They are flexible in reasoning.

• They require less data.

• They are always correct.

What is a weakness of monotonic systems?

• They may lack flexibility to retract conclusions.

• They are always reliable.

• They are easy to understand.

• They require no data.

What is suggested for practical applications of monotonic systems?

• Hybrid systems that integrate non-monotonic reasoning.

• Purely monotonic systems.

• Systems that discard all previous data.

• Systems that only use numerical data.

What is a common application of monotonic systems in healthcare?

• Patient data management.

• Predictive analytics for disease outbreaks.

• Real-time monitoring of vital signs.

• Automated billing systems.

How do monotonic systems handle new information?

• They incorporate it without changing previous conclusions.

• They discard all previous conclusions.

• They require manual updates to previous data.

• They ignore new information completely.

What is a benefit of using monotonic reasoning in AI?

• It simplifies the decision-making process.

• It allows for dynamic updates of conclusions.

• It enhances the complexity of reasoning.

• It guarantees the accuracy of all outputs.