AI 1 & 2 Marks Important Questions With Answers Artificial Intelligence
1 & 2 Marks (IMP)
1. What is Artificial Intelligence?
Artificial Intelligence (AI) is the branch of computer science that aims to create machines capable of intelligent behavior—such as learning, reasoning, and problem-solving.
2. What is AI Technique?
AI Technique refers to the method or strategy used to solve problems or make decisions in AI systems. It includes knowledge representation, search strategies, and learning methods.
3. Define an agent and agent function.
- Agent: An agent is anything that can perceive its environment through sensors and act upon that environment through actuators. It could be a robot, a software program, or even a human being in an AI context. The agent’s goal is to make decisions that maximize performance based on the information it gathers.
- Agent Function: An agent function is a mathematical description or mapping from percept sequences (the history of everything the agent has perceived) to actions. It defines what action an agent should take in response to any given percept sequence.
4. Differentiate an agent function and an agent program.
| Agent Function | Agent Program |
|---|---|
| Abstract mathematical mapping. | Concrete implementation (e.g., code). |
| Infinite lookup table in theory. | Runs on physical hardware. |
5. What are the different types of agents?
- Simple Reflex Agents
- Model-based Reflex Agents
- Goal-based Agents
- Utility-based Agents
- Learning Agents
6. Define Rational Agent.
A rational agent is one that acts to achieve the best expected outcome based on its knowledge, percepts, and goals.
7. List down the characteristics of intelligent agent.
- Autonomy
- Reactivity
- Pro-activeness
- Social ability
- Learning capability
8. What is Problem Reduction?
Problem reduction is breaking down a complex problem into smaller, more manageable sub-problems, solving them individually, and combining their solutions.
Example: Solving a Rubik’s cube layer-by-layer.
9. Define Goal Formulation and Problem Formulation.
- Goal Formulation: Goal formulation is the process of defining what the agent wants to achieve. It involves specifying the desired outcome or state that the agent should try to reach.
- Problem Formulation: Problem formulation is the process of defining the problem that needs to be solved to achieve the goal.
10. What is best-first search and state space search?
Best-First Search: A search algorithm that explores a graph by expanding the most promising node chosen according to a heuristic function.
State Space Search: A formal representation of a problem where each state represents a configuration, and actions move from one state to another.
11. What are the advantages and disadvantages of using breadth-first search in AI application?
| Advantages | Disadvantages |
|---|---|
| Guarantees shortest path (unweighted graphs). | High memory usage (stores all nodes). |
| Complete (finds a solution if it exists). | Slow for large graphs (O(b^d)). |
Advantages:
- Finds the shortest path in an unweighted graph.
- Guaranteed to find a solution if one exists.
Disadvantages:
- Requires a large amount of memory.
- Slow for large or infinite search spaces.
12. What is depth first Search?
- Depth-First Search (DFS) is a search algorithm used for traversing or searching tree or graph data structures.
- Uses a stack (LIFO) or recursion.
Example: Solving mazes, topological sorting.
13. What is Importance of Game Playing?
Provides a controlled environment for testing AI techniques.
Encourages the development of strategies, learning, and decision-making.
14. Challenges in Game Playing.
- Large state spaces.
- Uncertainty and opponent modeling.
- Need for real-time decision
15. Convert the given expression into postfix expression
- Heuristic Search is a search strategy that uses heuristics (rules of thumb or educated guesses) to guide the search process toward the goal more efficiently than uninformed (blind) search methods.
- A heuristic search uses a heuristic function h(n) to estimate the cost or distance from a node nn to the goal. It helps the algorithm decide which paths are more promising to explore first.
Heuristic search is an AI problem-solving technique that uses rules of thumb (heuristics) to guide the search process efficiently, reducing the time and computational effort required to find a solution. Unlike brute-force methods (e.g., BFS, DFS), heuristic search prioritizes paths that appear more promising based on a heuristic function (h(n)).
16. What is knowledge representation?
Knowledge Representation (KR) is a way to store, organize, and structure knowledge so that an AI system can understand, reason, and make decisions.
Knowledge Representation (KR) is a formal method to encode information about the world in a way that an AI system can understand, reason with, and use to solve problems.
17. What is the role of inference in knowledge representation?
Inference is the logical process of deriving new knowledge from existing facts using rules or logic. It allows the AI system to reason, draw conclusions, and solve problems.
Inference is the process of deriving new knowledge from existing knowledge using logical rules.
Roles:
Deduction: Apply rules to known facts (e.g., “Socrates is a human → Socrates is mortal”).
Abduction/Induction: Hypothesize likely conclusions (e.g., medical diagnosis).
18. What is propositional logic in knowledge representation?
Propositional logic represents knowledge using propositions (simple, true/false statements) and logical connectives (AND, OR, NOT, ⇒).
19. What is Predicate Logic?
Predicate Logic, also known as First-Order Logic (FOL), is a formal system in artificial intelligence and mathematics used to represent and reason about the properties of objects and their relationships.
20. Write Issues in knowledge representation and provide solution of any 2 issues.
| Issue | Solution |
|---|---|
| Representation Accuracy | Use expressive KR techniques (e.g. FOL) |
| Incomplete Knowledge | Apply default or probabilistic reasoning |
| Computational Efficiency | Use structured KR like semantic nets/frames |
21.What are semantic nets?
Semantic Nets (Semantic Networks) are a graph-based knowledge representation technique used in artificial intelligence to represent relationships between concepts.
22. What are frames? How do they differ from semantic nets?
Frames are data structures for representing stereotypical situations using slots and values.
Advantages of Frames:
Encapsulate detailed knowledge in a modular form
Support default reasoning and inheritance
Can include procedures or rules within slots
| Frames | Semantic Nets |
| Table-like, slot–filler representation | Graph-based, nodes and edges |
| Detailed representation of individual concepts | Relationships between concepts |
| Supports inheritance via parent frames | Supports inheritance through “is-a” links |
| More structured and detailed | More visual and general-purpose |
| Can include procedural rules in slots | No built-in support for procedures |