Mumbai University Solved Question Papers
Artificial Intelligence
Previous Year Question Paper with Solution
BSc CS · Sem 5
Mar 2026 Examination
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BSc CS Sem 5 Artificial Intelligence Mar 2026 Question Paper with Solutions
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First published on munotes.in on 17 October 2026.
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Model answers written and edited by the munotes.in editorial desk.
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The University does not publish an official answer key for this paper. The answers in this volume are model answers, written to show how a full-mark answer is built. They are a study aid, not an authority on what an examiner marked.
The question paper reproduced here is the paper as set by the University of Mumbai at the Mar 2026 examination.
Contents
The questions below are the paper as the University of Mumbai set it at the Mar 2026 examination, in the order it was set.
MarksPage
MarksPage
The Paper as Set
The questions in this volume are the questions asked at the Mar 2026 examination, reproduced as the University of Mumbai set them, in the order it set them. Nothing has been reworded, added or left out. Only the answers are ours. See the original question paper.
Duration 2½ hours · Total marks 75 · 24 questions answered
Instructions printed on the paper
- N.B. (1) All questions are compulsory. (2) Figures to the right indicate marks. (3) Illustrations, in-depth answers and diagrams will be appreciated. (4) Mixing of sub-questions is not allowed.
How to use this volume
Solve the paper first, under exam conditions and against the clock. Then read the answers here and mark your own. Reading a solution before attempting the question feels productive and teaches very little, because recognising an answer is not the same as being able to write one.
SECTION I
Q.1
Attempt ANY FOUR from the following · 20 Marks
(a)What is the role of PEAS factors in AI Agent? Mention it for Part picking robot and for playing Soccer Match.[5]
Answer
For full marks, cover: what PEAS stands for, why an agent designer writes it first, each of the four letters explained, and a complete PEAS table for both the part-picking robot and the soccer-playing agent.
What PEAS is
PEAS stands for Performance measure, Environment, Actuators and Sensors. It is the standard way to describe the task environment of an agent before the agent is designed.
The role of each factor
- Performance measure: the standard by which the agent's behaviour is judged. A rational agent chooses actions that are expected to maximise this measure, so it decides what "doing well" means.
- Environment: the world in which the agent works, including the objects and other agents in it.
- Actuators: the parts through which the agent acts on the environment, such as motors, arms or a display.
- Sensors: the parts through which the agent perceives the environment, such as cameras or touch sensors.
Writing PEAS first matters because the right design depends on all four. The same program can be rational in one environment and foolish in another.
PEAS for a part-picking robot
| Factor | Part-picking robot |
|---|---|
| Performance measure | percentage of parts placed in the correct bins |
| Environment | conveyor belt carrying parts, and the bins |
| Actuators | jointed arm and hand (gripper) |
| Sensors | camera, joint angle sensors |
PEAS for playing a soccer match
| Factor | Soccer-playing agent |
|---|---|
| Performance measure | goals scored minus goals conceded, matches won, fair play (no fouls) |
| Environment | the field, the ball, its own team, the opponents, the referee |
| Actuators | motors for running and turning, legs or kicker for kicking and passing |
| Sensors | cameras, ball and player position sensors, touch sensors, accelerometer and orientation sensors |
The soccer environment is much harder. It is multi-agent, dynamic (the ball and players keep moving), stochastic and only partially observable. The part-picking robot's world is closer to episodic: each part is decided on its own.
Conclusion. PEAS (Performance measure, Environment, Actuators, Sensors) specifies an agent's task environment and is the first step of agent design. For a part-picking robot it is correct bins, a conveyor and bins, an arm and gripper, and a camera with joint sensors. For a soccer agent it is goals and wins, a field with players and a ball, and motors for running and kicking. Its sensors are cameras with position and touch sensors.
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