Introductory lab of the Artificial Intelligence Lab course (BSCS, Air University). The lab sets up the Python environment used throughout the course and covers the core language fundamentals — variables, data structures, control flow, and functions — followed by a first look at NumPy arrays and vectorized operations.
The exercises are framed around a simple reflex vacuum-cleaner agent ("ReflexBot"): battery levels, room grids, and percept-to-action rules give the basic Python concepts an agent-based context that later labs build on.
- Verify the Python scientific stack used in this course (NumPy, Pandas, Matplotlib, scikit-learn, NetworkX)
- Declare variables of different types and inspect them with
type() - Convert between
str,int, andfloat - Use arithmetic and logical operators
- Control program flow with
if/elif/else,for, andwhile(includingbreak/continue) - Work with the built-in collections:
list,tuple, anddict - Write functions, including a rule-based simple reflex agent
- Create NumPy arrays and apply vectorized operations (
np.diff, boolean masking,np.mean)
| # | File | Concept | What it does |
|---|---|---|---|
| 1 | lab01.py |
Library environment check | Imports NumPy, Pandas, Matplotlib, scikit-learn, and NetworkX and prints each version |
| 2 | vraibles.py |
Variables & data types | Stores an agent name, battery level, temperature, and an active flag; prints each value with its type |
| 3 | typeconversion.py |
Type conversion | Converts "87" → 87 → 87.0 and checks types |
| 4 | operators.py |
Operators | Arithmetic (+, -, **, //, %) and a logical and check against the battery level imported from vraibles.py |
| 5 | conditions.py |
Conditional statements | battery_status() classifies a battery level as high / medium / low using if/elif/else |
| 6 | forloop.py |
for loop |
Iterates over a list of visited rooms |
| 7 | whileloop.py |
while loop |
Simulates battery drain with continue (skip 40) and break (stop at ≤ 10) |
| 8 | List.py |
Lists | Builds a percept list (dust, wall, clean_floor) and appends obstacles |
| 9 | tuples.py |
Tuples | Stores a grid position (2, 3) as an immutable tuple |
| 10 | dictionaries.py |
Dictionaries | Models agent state (battery, location, mode) as a dict and reads a value by key |
| 11 | functions.py |
Functions | simple_reflex_agent(percept) maps percepts to actions (dust → clean, wall → turn, …) via a rule dictionary |
| 12 | nestedarray.py |
NumPy 2D arrays | Creates a 3×3 room grid (1 = dirty cell) and prints the array and its shape |
| 13 | vectorizedoperations.py |
Vectorized operations | Battery readings array: per-step drop with np.diff, readings below 80 via boolean mask, and the mean |
- Python 3
- NumPy — arrays and vectorized operations
- Pandas, Matplotlib, scikit-learn, NetworkX — verified in
lab01.py; used in later labs
Most scripts (Tasks 2–11) use only the Python standard library. NumPy is required for Tasks 1, 12, and 13.
ai-lab-01/
├── lab01.py # Library environment check
├── vraibles.py # Variables and data types
├── typeconversion.py # Type conversion
├── operators.py # Arithmetic and logical operators
├── conditions.py # if / elif / else
├── forloop.py # for loop
├── whileloop.py # while loop with break/continue
├── List.py # Lists
├── tuples.py # Tuples
├── dictionaries.py # Dictionaries
├── functions.py # Functions — simple reflex agent
├── nestedarray.py # NumPy 2D arrays
└── vectorizedoperations.py # NumPy vectorized operations
Requires Python 3. To run the NumPy and library-check scripts, install the scientific stack:
pip install numpy pandas matplotlib scikit-learn networkxEvery script is standalone — run it from the project directory:
python lab01.py
python vraibles.py
python typeconversion.py
python operators.py
python conditions.py
python forloop.py
python whileloop.py
python List.py
python tuples.py
python dictionaries.py
python functions.py
python nestedarray.py
python vectorizedoperations.pyNote:
operators.pyimportsbattery_levelfromvraibles.py, so running it first prints the variable/type lines from that file.
lab01.py (versions will differ by machine):
Numpy:, 1.26.4
Pandas:, 2.1.4
Matplotlib:, 3.6.3
Sklearn:, 1.9.1
Networkx:, 3.7
All core librarires loaded Successfully.
functions.py — the simple reflex agent's rule table in action:
dust -> clean
wall -> turn
Clean_floor -> no action
obstacles -> turn
vectorizedoperations.py:
Drop per step: [-8 -8 -8 -8 -8]
Below 80 : [76 68 60]
mean: 80.0
- The rule lookup in
functions.pyis case-sensitive:"Clean_floor"(capital C) returnsno actionbecause the rule key is lowercase"clean_floor". - In
nestedarray.py, the final print statement outputs only its label — the dirty-cell count itself is not computed. - Original coursework filenames are kept as submitted (e.g.
vraibles.py), andoperators.pydepends on it by import, so the names should not be changed without updating that import.
AI Lab 01 — Artificial Intelligence Lab, BSCS (5th semester), Air University, Islamabad.