1) Define Artificial Intelligence. Explain different perspectives of AI. (6 Marks)
Answer:
Artificial Intelligence (AI) is the simulation of human intelligence by machines. It includes:
Ability to solve problems
Ability to act rationally
Ability to act like humans
Different Perspectives of AI:
Acting Humanly (Turing Test Approach)
Proposed by Alan Turing.
A machine is intelligent if it can imitate human responses.
Requires NLP, knowledge representation, reasoning, and learning.
Thinking Humanly (Cognitive Modeling Approach)
Focuses on how humans think.
Uses psychology and brain studies.
Example: General Problem Solver (Newell & Simon).
Thinking Rationally (Laws of Thought)
Based on formal logic (Aristotle).
Uses logical reasoning to derive conclusions.
Acting Rationally (Rational Agent Approach)
An agent acts to maximize performance.
Focus is on correct action rather than human-like action.
2) What is an Intelligent Agent? Explain PEAS with example. (6 Marks)
Answer:
An Agent is anything that perceives its environment through sensors and acts upon the environment through actuators.
Important Terms:
Percept – Input from environment
Agent Function – Mapping from percept history to action
Performance Measure – Criteria for success
PEAS Description (Automated Taxi Driver)
P – Performance: Safe, fast, legal, comfortable ride, profit
E – Environment: Roads, traffic, pedestrians, customers
A – Actuators: Steering, brake, accelerator, horn
S – Sensors: Camera, GPS, speedometer, engine sensors
PEAS helps design task environments for agents.
3) Explain types of agents. (6 Marks)
Answer:
Simple Reflex Agent
Uses condition-action rules.
Works in fully observable environments.
Model-Based Reflex Agent
Maintains internal state.
Works in partially observable environments.
Goal-Based Agent
Works towards achieving goals.
Uses search and planning.
Utility-Based Agent
Maximizes utility function.
Chooses best action among alternatives.
Learning Agent
Improves performance over time.
Components:
Learning element
Critic
Performance element
Problem generator
4) Explain Problem Solving Agent and its phases. (6 Marks)
Answer:
A Problem Solving Agent (PSA) finds a sequence of actions to reach a goal state.
Phases:
Goal Formulation
Decide what goal to achieve.
Problem Formulation
Define:
Initial state
Goal state
Actions
Transition model
Path cost
Search Phase
Find best sequence of actions.
Execution Phase
Execute selected actions.
Example: Route finding from Arad to Bucharest.
5) Compare BFS and DFS. (6 Marks)
Feature
BFS
DFS
Strategy
FIFO
LIFO
Completeness
Yes
No
Optimality
Yes (unit cost)
No
Time Complexity
O(b^d)
O(b^m)
Space Complexity
O(b^d)
O(bm)
Memory Usage
High
Low
BFS finds shortest path.
DFS uses less memory but may get stuck in deep paths.
6) Explain A* Algorithm and Admissibility. (6 Marks)
Answer:
A* is an informed search algorithm.
Evaluation Function:
g(n) – Cost so far
h(n) – Estimated cost to goal
f(n) – Estimated total cost
Properties:
Complete
Optimal (if heuristic is admissible)
Admissible Heuristic:
A heuristic is admissible if:
7) Explain Wumpus World. (6 Marks)
Answer:
Wumpus World is a 4×4 grid environment used for knowledge-based agents.
Performance Measure:
+1000 for gold
-1000 for death
-1 per action
-10 for arrow
Sensors:
Stench (near Wumpus)
Breeze (near pit)
Glitter (gold present)
Bump (hit wall)
Scream (Wumpus killed)
Actuators:
Move forward
Turn left/right
Grab
Shoot
Climb
Agent uses logic and inference to safely find gold.
8) Explain Uninformed vs Informed Search. (6 Marks)
Uninformed Search:
No domain knowledge.
Examples:
BFS
DFS
Uniform Cost Search
Informed Search:
Uses heuristic function.
Examples:
Greedy Best First
A*
Hill Climbing
Informed search reduces search space using heuristic knowledge.
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