Introduction of AI

[Virtual Presenter] Introduction of A-I Name: venkatesh Rollno: 2241702.

[Virtual Presenter] Contents Representing knowledge using rules Rules based deduction systems . Reasoning under uncertainty,.

[Virtual Presenter] Representing knowledge using rules Knowledge representation in A-I refers to the methods used to encode information about the world into a format that an A-I system can understand and use to make decisions. It is a crucial component of A-I that bridges the gap between raw data and meaningful reasoning. By representing knowledge in a structured way, A-I systems can interpret data, draw inferences, and apply reasoning techniques to solve problems..

[Audio] Rules are IF–THEN statements IF (condition) → T-H-E-N (action/conclusion) Easy to understand and implement Closely resembles human reasoning Example: IF traffic signal is red T-H-E-N vehicles must stop.

[Audio] Rules based deduction systems A Rule Based System is an A-I system that makes decisions using a set of predefined rules, usually in the form of IF-THEN statements. These rules simulate human reasoning and decision making by following logical steps..

[Audio] Components of Rule Based Systems Rule Base – Collection of rules Working Memory – Stores facts Inference Engine – Applies rules to facts User Interface – Interaction with user Rule Based Deduction Systems Deduction: deriving conclusions from known facts Uses logical inference Two main inference techniques: Forward Chaining Backward Chaining.

[Audio] Examples--Medical Diagnosis Rules: IF fever A-N-D headache → malaria IF fever A-N-D body pain → viral fever Facts: Patient has fever Patient has headache Conclusion: Patient has malaria.

[Audio] Reasoning under uncertainty, Reasoning under uncertainty is the process of making decisions when we do not have complete or perfect information. In real life, many situations involve doubt, chance, or missing data. Even with uncertainty, we must still choose the best possible action. This type of reasoning helps reduce errors and improve decision making. It is widely used in daily life, science, and artificial intelligence..

[Audio] Simple Examples of Uncertainty Weather prediction Medical diagnosis Traffic conditions Exam results Conclusion Rules help represent knowledge clearly Deduction systems help find conclusions Uncertainty reasoning helps in real life situations All are important in intelligent systems.