Introduction to Artificial Intelligence

This course aims to deliver a comprehensive overview of Artificial Intelligence, its implications, applications, and the skills to leverage it. The course begins by describing what the latest generation of artificial intelligence techniques can do. After an introduction to some basic concepts and techniques, the course illustrates both the potential and current limitations of these techniques with examples from a variety of applications.

We spend some time on understanding the strengths and weaknesses of human decision-making and learning, specifically in combination with AI systems. Exercises will include hands-on application of basic AI techniques as well as selection of appropriate technologies for a given problem and anticipation of design implications. In a final project, groups of students will participate in the creation of a simple AI-based application.

Prerequisite: EP16.TOKT11108 (Fundamental Programming Concepts in Python)

The course project constitutes a significant part of the midterm evaluation (20%). Each team selects one topic from the provided project list. The project consists of two main parts:

1. Presentation: 10–12 minutes per team, to be delivered next week.
2. Report: Due one week after the presentation date.

Submit the presentation and report (all .pdf files) via email or Zalo. There are awards for best presentation and best report.

Project workflow

1. Identify a real-world problem.
2. Model it as a search problem (state space search).
3. Apply at least 2–3 search algorithms (BFS, DFS, Greedy, A*, etc.).
4. Implement and test with code.
5. Compare results (expanded nodes, time, solution quality).
6. Relate findings back to the real-world context.

Suggested report structure

1. Introduction: Real-world background of the problem.
2. Problem Formulation: Search problem representation.
3. Algorithms: Description of applied algorithms.
4. Experiments & Results: Comparison table and visual examples.
5. Discussion: Analysis of results and insights.
6. Response to Reviewers: Questions and answers from the presentation.
7. Conclusion: Summary and findings.

Project example: the 8-Puzzle

Purely theoretical version. "Initial state → Goal state. Apply BFS, GBFS (heuristic = misplaced tiles), A* (heuristic = Manhattan distance). Compare number of expanded nodes and solution depth."

Real-world interpretation. The 8-Puzzle can be modeled as seat arrangement in an international conference. There are 9 chairs (8 delegates + 1 empty spot). The organizer must arrange guests so that each delegate sits in the correct assigned seat. A valid operation is swapping a delegate with an adjacent empty chair. The goal is the correct seating arrangement with the fewest moves.

Sample introduction for the report. "In international conferences, the correct arrangement of delegates according to diplomatic protocols is essential. We can model this seating arrangement task as the 8-Puzzle problem, where each tile represents a delegate and the empty tile represents an unoccupied chair. The objective is to reach the final correct arrangement with minimal movements. In this study, we apply classical search algorithms (BFS, GBFS, A*) and compare their performance in terms of solution depth and number of expanded nodes, thereby evaluating the effectiveness of AI search methods for real-world organizational tasks."

Important notes

• Creativity is required. Every project must be grounded in a real-world scenario (politics, society, traffic, healthcare, environment, education, etc.), not only in abstract puzzles.
• The "Response to Reviewers" section is mandatory. This is what makes your project unique compared to standard coursework.
• Visualization is encouraged. Teams are encouraged to use visualizations, simulations, or examples to make the application clearer.

Project submission

Upload your project files in PDF format:

• Submit both your presentation slides and final report as PDF files.
• File naming convention: TeamName_Presentation.pdf and TeamName_Report.pdf.
• Deadline: report due one week after the presentation date.

Upload project files to Google Drive

You will need to sign in with your account to upload files. If you encounter any issues, please contact the instructors.