INTRODUCTI
ON TO A.I.
Eevi Mellimi
, Essay Questions
1. (a) Define Artificial Intelligence. Discuss the history of AI and the "Turing Test" as a functional
measure of machine intelligence.
OR
(b) Explain the various approaches to AI: Thinking Humanly, Thinking Rationally, Acting Humanly,
and Acting Rationally.
2. (a) What is an Intelligent Agent? Discuss the PEAS description of task environments and provide
examples for an Automated Taxi and a Medical Diagnosis System.
OR
(b) Describe the different types of Agent Programs: Simple Reflex Agents, Model-based Agents,
Goal-based Agents, and Utility-based Agents.
3. (a) Explain the Breadth-First Search (BFS) algorithm. Discuss its performance in terms of
completeness, optimality, time complexity, and space complexity.
OR
(b) Discuss the Depth-First Search (DFS) and Depth-Limited Search algorithms. Why is DFS often
preferred for memory-constrained environments?
4. (a) Describe the A* Search algorithm. Explain how the evaluation function $f(n) = g(n) + h(n)$
helps in finding the optimal path efficiently.
OR
(b) Explain the Minimax Algorithm in game playing. Discuss how Alpha-Beta Pruning improves the
performance of adversarial search.
5. (a) Explain Propositional Logic and its syntax. Discuss the use of Truth Tables for checking the
validity and satisfiability of logical sentences.
OR
(b) What is Knowledge Representation? Discuss the components of a Knowledge-Based System and
the role of an Inference Engine.
Short Questions
6. Distinguish between a "Simple Reflex Agent" and a "Model-based Agent."
7. Define the branching factor ($b$) and depth ($d$) in the context of search tree complexity.
8. What is a "Heuristic Function"? Provide a real-world example.
9. Explain the concept of "Completeness" in search algorithms.
10. Define "Knowledge Representation" and its basic requirements in AI.
11. What is "Alpha-Beta Pruning" and why is it used in game trees?
12. Briefly explain the mechanism of "Forward Chaining" in logic.
, ANSWER S
1. a) Define AI. Discuss the history of AI and the "Turing Test."
Definition of Artificial Intelligence Artificial Intelligence (AI) is the branch of computer science that
aims to create systems capable of performing tasks that normally require human intelligence, such as visual
perception, speech recognition, and decision-making. It is the study of "Intelligent Agents"—systems that
perceive their environment and take actions that maximize their chance of success.
The Evolution of AI The history of AI began with the Gestation period (1943–1955) where the first
mathematical model of a biological neuron was created. The field was officially named at the Dartmouth
Workshop in 1956. Since then, it has gone through periods of high expectations, followed by "AI Winters"
where funding was cut due to slow progress. Today, we are in the era of Deep Learning and Big Data,
where AI is integrated into almost every digital service.
The Turing Test Measure Proposed by Alan Turing in 1950, this test provides a functional definition of
intelligence. A human judge engages in a natural language conversation with a human and a machine via a
text channel. If the judge cannot reliably distinguish the machine from the human, the machine is said to
possess "intelligence." To pass, a machine needs Natural Language Processing, Knowledge Representation,
and Automated Reasoning.
Strategic Conclusion The Turing Test remains the most famous philosophical benchmark for AI. While
modern research often focuses on "Rationality" (doing the right thing) rather than just "Mimicry" (looking
human), the test was the first to suggest that intelligence could be measured by external behavior rather
than internal biological processes.
1. b) Explain the various approaches to AI (Thinking/Acting Humanly/Rationally).
The Four Approaches to AI AI researchers generally follow one of four main paths based on whether
they want the system to mimic human behavior or follow pure mathematical logic (rationality).
Human-Centered Approaches
• Acting Humanly: This is the "Turing Test" approach. It focuses on making machines perform tasks as
well as humans do. It is about the output and behavior, not the internal process.
• Thinking Humanly: This is the "Cognitive Modeling" approach. It uses psychological experiments
and brain scans to understand how humans think and tries to recreate those exact thought
processes in a computer.
Rationality-Centered Approaches
• Thinking Rationally: This is the "Laws of Thought" approach. It relies on formal logic and syllogisms
(e.g., If A=B and B=C, then A=C). The goal is to reach a correct conclusion based on logical premises.
• Acting Rationally: This is the "Rational Agent" approach. An agent is rational if it acts to achieve the
best outcome. This doesn't require the machine to "think" like a human; it only requires the
machine to make the most efficient decision possible.
ON TO A.I.
Eevi Mellimi
, Essay Questions
1. (a) Define Artificial Intelligence. Discuss the history of AI and the "Turing Test" as a functional
measure of machine intelligence.
OR
(b) Explain the various approaches to AI: Thinking Humanly, Thinking Rationally, Acting Humanly,
and Acting Rationally.
2. (a) What is an Intelligent Agent? Discuss the PEAS description of task environments and provide
examples for an Automated Taxi and a Medical Diagnosis System.
OR
(b) Describe the different types of Agent Programs: Simple Reflex Agents, Model-based Agents,
Goal-based Agents, and Utility-based Agents.
3. (a) Explain the Breadth-First Search (BFS) algorithm. Discuss its performance in terms of
completeness, optimality, time complexity, and space complexity.
OR
(b) Discuss the Depth-First Search (DFS) and Depth-Limited Search algorithms. Why is DFS often
preferred for memory-constrained environments?
4. (a) Describe the A* Search algorithm. Explain how the evaluation function $f(n) = g(n) + h(n)$
helps in finding the optimal path efficiently.
OR
(b) Explain the Minimax Algorithm in game playing. Discuss how Alpha-Beta Pruning improves the
performance of adversarial search.
5. (a) Explain Propositional Logic and its syntax. Discuss the use of Truth Tables for checking the
validity and satisfiability of logical sentences.
OR
(b) What is Knowledge Representation? Discuss the components of a Knowledge-Based System and
the role of an Inference Engine.
Short Questions
6. Distinguish between a "Simple Reflex Agent" and a "Model-based Agent."
7. Define the branching factor ($b$) and depth ($d$) in the context of search tree complexity.
8. What is a "Heuristic Function"? Provide a real-world example.
9. Explain the concept of "Completeness" in search algorithms.
10. Define "Knowledge Representation" and its basic requirements in AI.
11. What is "Alpha-Beta Pruning" and why is it used in game trees?
12. Briefly explain the mechanism of "Forward Chaining" in logic.
, ANSWER S
1. a) Define AI. Discuss the history of AI and the "Turing Test."
Definition of Artificial Intelligence Artificial Intelligence (AI) is the branch of computer science that
aims to create systems capable of performing tasks that normally require human intelligence, such as visual
perception, speech recognition, and decision-making. It is the study of "Intelligent Agents"—systems that
perceive their environment and take actions that maximize their chance of success.
The Evolution of AI The history of AI began with the Gestation period (1943–1955) where the first
mathematical model of a biological neuron was created. The field was officially named at the Dartmouth
Workshop in 1956. Since then, it has gone through periods of high expectations, followed by "AI Winters"
where funding was cut due to slow progress. Today, we are in the era of Deep Learning and Big Data,
where AI is integrated into almost every digital service.
The Turing Test Measure Proposed by Alan Turing in 1950, this test provides a functional definition of
intelligence. A human judge engages in a natural language conversation with a human and a machine via a
text channel. If the judge cannot reliably distinguish the machine from the human, the machine is said to
possess "intelligence." To pass, a machine needs Natural Language Processing, Knowledge Representation,
and Automated Reasoning.
Strategic Conclusion The Turing Test remains the most famous philosophical benchmark for AI. While
modern research often focuses on "Rationality" (doing the right thing) rather than just "Mimicry" (looking
human), the test was the first to suggest that intelligence could be measured by external behavior rather
than internal biological processes.
1. b) Explain the various approaches to AI (Thinking/Acting Humanly/Rationally).
The Four Approaches to AI AI researchers generally follow one of four main paths based on whether
they want the system to mimic human behavior or follow pure mathematical logic (rationality).
Human-Centered Approaches
• Acting Humanly: This is the "Turing Test" approach. It focuses on making machines perform tasks as
well as humans do. It is about the output and behavior, not the internal process.
• Thinking Humanly: This is the "Cognitive Modeling" approach. It uses psychological experiments
and brain scans to understand how humans think and tries to recreate those exact thought
processes in a computer.
Rationality-Centered Approaches
• Thinking Rationally: This is the "Laws of Thought" approach. It relies on formal logic and syllogisms
(e.g., If A=B and B=C, then A=C). The goal is to reach a correct conclusion based on logical premises.
• Acting Rationally: This is the "Rational Agent" approach. An agent is rational if it acts to achieve the
best outcome. This doesn't require the machine to "think" like a human; it only requires the
machine to make the most efficient decision possible.
