https://github.com/amirreza81/artificial-intelligence

Solutions to practical assignments of Artificial Intelligence course (CE-417) at Sharif University of Technology
https://github.com/amirreza81/artificial-intelligence

a-star a-star-algorithm adversarial-search ai alpha-beta-pruning artificial-intelligence bayesian-networks cryptarithmetic decision-trees deep-learning logistic-regression machine-learning minimax-algorithm pytorch q-learning q-learning-vs-sarsa rainforcement-learning rl simulated-annealing simulated-annealing-algorithm

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Solutions to practical assignments of Artificial Intelligence course (CE-417) at Sharif University of Technology

• Host: GitHub
• URL: https://github.com/amirreza81/artificial-intelligence
• Owner: Amirreza81
• Created: 2023-11-28T09:27:18.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-28T11:28:28.000Z (6 months ago)
• Last Synced: 2025-04-01T13:21:19.690Z (26 days ago)
• Topics: a-star, a-star-algorithm, adversarial-search, ai, alpha-beta-pruning, artificial-intelligence, bayesian-networks, cryptarithmetic, decision-trees, deep-learning, logistic-regression, machine-learning, minimax-algorithm, pytorch, q-learning, q-learning-vs-sarsa, rainforcement-learning, rl, simulated-annealing, simulated-annealing-algorithm
• Language: Jupyter Notebook
• Homepage:
• Size: 25 MB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Artificial Intelligence 🤖🧠

**Solutions to practical assignments of Artificial Intelligence course (CE-417) at Sharif University of Technology**

This repository contains solutions for the practical assignments given in the AI course, covering various essential topics in artificial intelligence. Each assignment implements a specific AI technique or algorithm to solve real-world problems, showcasing the theoretical concepts in action.

## Table of Contents

> - [A Star](#A\*)

> - [Simulated Annealing](#Simulated_Annealing)

> - [Cryptarithmetic Puzzle](#Cryptarithmetic_Puzzle)

> - [Adversarial Search](#Adversarial_Search)

> - [Q_Tabular](#Q_Tabular)

> - [RL_Chat](#RL_Chat)

> - [Bayesian Networks](#Bayesian_Networks)

> - [Decision Tree](#Decision_Tree)

> - [Logistic Regression](#Logistic_Regression)

* ### **A\***:

  Implements the A* search algorithm to solve an elevator scheduling problem where students and professors must be transported between two floors, following specific constraints.

    

  ![A* Algorithm GIF](a_star_algorithm.gif)

    

  **Problem Description**:

  The elevator needs to move students and professors between two floors with the following constraints:

  - No floor should have more students than professors (if professors are present).

   - The solution must ensure that all students and professors are transferred safely across floors.

  **Approach**:

  The problem is represented as a graph, where each state is described by the number of students and professors on each floor, and the position of the elevator. A* uses a heuristic to search for the 

   optimal path from the initial to the final state, ensuring efficiency and optimality.

      

  **Comparison with DFS**:  

      The A* algorithm outperforms DFS in terms of finding shorter paths, thanks to its heuristic function, though it explores more states.

  See this [file](https://github.com/Amirreza81/Artificial-Intelligence/blob/main/A-star/A_star.ipynb) for more details.

* ### **Simulated_Annealing**:

  Demonstrates optimization using the Simulated Annealing algorithm for solving the Knapsack problem.

    

  **Problem Description**:

      The Knapsack problem involves selecting a subset of items with specific weights and values to maximize the total value without exceeding a given weight limit.

  **Approach**:

      Simulated Annealing is a probabilistic algorithm that explores the solution space by accepting worse solutions with decreasing probability over time (as the temperature cools). This helps escape 

  local optima and leads to a near-optimal solution.

  **Results**:  

      The algorithm finds an average best solution with a value of approximately 2595.05 over multiple runs, demonstrating its effectiveness in solving the Knapsack problem.

  See this [file](https://github.com/Amirreza81/Artificial-Intelligence/blob/main/Simulated%20Annealing/Simulated_Annealing.ipynb) for more details.

* ### **Cryptarithmetic_Puzzle**:  

    The Cryptarithmetic Puzzle is a puzzle where the digits of some numbers are replaced with letters to make a mathematical equation. Each letter represents a unique digit from 0 to 9 (in case of base 10). The puzzle has the following constraints:

    - Each letter can be replaced by a digit from 0 to 9 (in case of base 10), but no digit can be used twice.

    - Each letter must be replaced by the same digit throughout the puzzle.

    - The leftmost digit of each number cannot be zero.

    

    

* ### **Adversarial_Search**:  

    Minimax algorithm for game-playing agents with alpha-beta pruning.

    

* ### **Q_Tabular**:  

    Implementation of Q-learning with tabular updates for reinforcement learning.

    

* ### **RL_Chat**:  

    A simple chat system utilizing reinforcement learning for decision-making.

* ### **Bayesian_Networks**:

    Building and inference on Bayesian networks for probabilistic reasoning.

* ### **Decision_Tree**:

    Implementing decision tree algorithms for classification tasks.

* ### **Logistic_Regression**:

    In this notebook I implemented Logistic Regression from scratch. First I implemented all the needed function in order to use Logistic Regression. After that I tested my implementation on a dummy dataset and visualize the model boundary. Then I did the same on a tweet sentiment classification dataset.

    **Steps**:

        - Creating Dummy Dataset

        - Implementing model

        - Implementing training

        - Training on a Tweet Sentiment Analysis Dataset (Ungraded)

    **First dataset**:

    ![Dataset](./Logistic%20Regression/images/dataset.png)

    ![Result](./Logistic%20Regression/images/resultLR.png)

    

    **Second dataset**:

    ![Dataset](./Logistic%20Regression/images/ds2.png)

    ![Result](./Logistic%20Regression/images/resultLR2.png)

    **Tweet Sentiment Analysis Dataset**:

    In this part I used the logistic regression model I implemented to classify whether a *tweet* contains a postivie or negative sentiment.

    The accuracy:

    ![Acc](./Logistic%20Regression/images/acc.png)

    The result:

    ![res](./Logistic%20Regression/images/res.png)

## Instructor ✍

[Professor Mohammad Hossein Rohban](https://scholar.google.com/citations?user=pRyJ6FkAAAAJ&hl=en)

## Contribution 👥

Feel free to fork this repository, submit pull requests, or raise issues if you find any bugs or have suggestions for improvements.