Artificial intelligence and expert system, Exams of Artificial Intelligence

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AI & EXPERT SYSTEMS

One-Day Revision & Exam Guide

BCA Semester IV - PRSU Prepared for Abhishek

Unit I: Introduction & Logic

Q1: Define Artificial Intelligence. What is the Turing Test?

 Definition: Artificial Intelligence (AI) is the branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as decision-making, problem-solving, and learning.  The Turing Test: Proposed by Alan Turing, it is a test to determine if a machine can exhibit intelligent behavior indistinguishable from a human. A human evaluator judges natural language conversations. If the evaluator cannot reliably tell the machine from the human, the machine passes the test.

Q2: What is the difference between Propositional Logic

and Predicate Logic?

 Propositional Logic: Deals with simple statements (propositions) that are either True or False. Example: "It is raining". It doesn't break down the sentence further.  Predicate Logic (First-Order Logic): A more advanced system that looks at the relationship between objects. It uses variables and quantifiers. Example: Instead of just P, it uses Raining(Today).

Unit II: AI Programming Languages & Uncertainties

Q3: Compare LISP and PROLOG.

Feature LISP (LISt Processing) PROLOG (PROgramming in LOGic) Paradigm Functional programming language. Logic programming language. Data Structures Uses lists and atoms. Uses facts, rules, and queries. Execution Based on evaluating functions. Based on pattern matching and backtracking. Best Used For Machine learning, complex math. NLP, Expert Systems. Syntax Uses heavy parentheses (like (this)). Uses standard logical statements.

 Scripts: A structured framework describing a predictable sequence of events (e.g., a Restaurant Script).

Unit IV: Expert Systems

Q9: What is an Expert System? Describe its architecture.

An AI program designed to mimic the decision-making ability of a human expert. Architecture Components:  Knowledge Base: Stores factual info and IF-THEN rules.  Inference Engine: Processes queries using Forward/Backward chaining.  User Interface: How the human interacts with the system.  Working Memory: Temporary database for the current problem.

Q10: Differentiate between Forward Chaining and

Backward Chaining.

Feature Forward Chaining Backward Chaining Approach Data-driven reasoning. Goal-driven reasoning. Starting Point Starts with known facts. Starts with a hypothesis/goal. Process Applies rules forward to derive new conclusions. Works backward to check if evidence supports the goal. Best Used For Planning, forecasting. Diagnostic systems.

Q11: Explain Pattern Recognition / Computer Vision

Pipeline.

 Low-Level: Pixel operations, edge detection.  Medium-Level: Segmentation, separating foreground from background.  High-Level: Interpreting objects using the knowledge base (e.g., recognizing a stop sign).

Exam Strategy & Final Tips

1. 1. Draw Diagrams: For Expert Systems and Production Systems, always draw the block architecture.
2. 1. Use Tables: Use the LISP vs PROLOG and Forward vs Backward chaining tables provided above.
3. 1. Attempt Everything: If you forget an exact algorithm, write down the broader concept (e.g., explain Heuristic search if you forget A*).
4. 1. Bullet Points: Write in distinct points and underline keywords like "Inference Engine" and "Local Maxima".