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Prerequisites
Taught by
Language of instruction
Duration
Identical courses
Cannot be taken as part of an examination contractAims
After succesful completion of this course, a student will
- have deep knowledge and insight into fundamental techniques from Artificial Intelligence, including: basic search methods, heuristic search methods, optimal path search methods, game tree search techniques, constraint solving techniques, planning techniques and markov decision processes;
- be able to simulate algorithms for each of the above techniques with pen and paper on small new examples;
- be able to implement heuristic, optimal and game-tree search methods in a provided programming environment;
- have insight in the relations between these techniques;
- have insight into the relevance of these techniques for applications.
Previous knowledge
Some familiarity with algorithms and data structures. Limited experience with a programming language.
Identical courses
This course is identical to the following courses:
H02A0C : Fundamentals of Artificial Intelligence
Is included in these courses of study
- Master of Artificial Intelligence (Leuven) (Specialisation: Big Data Analytics (BDA)) 60 ects.
- Master of Artificial Intelligence (Leuven) (Specialisation: Engineering and Computer Science (ECS)) 60 ects.
- Master of Bioinformatics (Leuven) (BIOSCIENCE ENGINEERING) 120 ects.
- Master of Bioinformatics (Leuven) (ENGINEERING) 120 ects.
- Courses for Exchange Students Faculty of Engineering Science (Leuven)
- Master of Engineering: Computer Science (Leuven) 120 ects.
- Master of Mobility and Supply Chain Engineering (Leuven) 120 ects.
Activities
3 ects. Fundamentals of Artificial Intelligence: Lecture (B-KUL-H02A0a)
Content
1. Introduction
- Definition and general context, both of the domain and the course
2. State-space representation and search methods
- state-space representation: introduction and trade-offs,
- blind search,
- heuristic search, including the study of the A*-algorithm,
- advanced aspects of heuristic search,
3. Examples of search
- search in data mining: pattern mining
- heuristic search in games
4. Constraint propagation
- backtracking, backtrack variants, intelligent and dependency directed backtracking,
- arc consistency techniques,
- hybrid constraint propagation methods
5. Some case studies of the use of constraint processing
- interpretation of line drawings,
- interpretation of natural language
6. Planning and Temporal representation
- partial-order regression planning: STRIPS
Course material
Copies of the slides are made available by the student organisation VTK.
Is also included in other courses
1 ects. Fundamentals of Artificial Intelligence: Exercises (B-KUL-H02K1a)
Content
1. Introduction (2 u.)
- Definition and general context, both of the domain and the course
2. State-space representation and search methods (8 u.)
- state-space representation: introduction and trade-offs,
- blind search,
- heuristic search, including the study of the A*-algorithm,
- advanced aspects of heuristic search,
- heuristic search in games
3. Examples of search: pattern mining and games (2 u.)
4. Constraint propagation (3 u.)
- backtracking, backtrack variants, intelligent and dependency directed backtracking,
- arc consistency techniques,
- hybrid constraint propagation methods
5. Some case studies of the use of constraint processing (3 u.)
- interpretation of line drawings,
- interpretatie of natural language
6. Planning and Temporal representation (1.5 u.)
- partial-order regression planning: STRIPS
Course material
Slides of the study material are available from the student organisation VTK
Is also included in other courses
1 ects. Fundamentals of Artificial Intelligence: Project (B-KUL-H0O43a)
Content
The projects challenge the student to implement AI search techniques seen in class, in a Python programming environment based on the PacMan world developed at UC Berkeley.
The project consists of two parts:
- P1: Search: Students implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world.
- P2: Multi-Agent Search: Classic Pacman is modeled as both an adversarial and a stochastic search problem. Students implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.
You will fill in portions of provided Python files during the assignment. An autograder is provided to grade your own answers and guide you in your development; your submission will be evaluated by us
independently. Collaboration and plagiarism are not allowed, we will check for this extensively and sanction if needed.
Course material
The project assignment and the necessary software libraries and installation instructions will be made available on Toledo.
Evaluation
Evaluation: Fundamentals of Artificial Intelligence (B-KUL-H22A0a)
Explanation
Your final grade is determined by three parts: the project, the lecture exam and the exercise exam. The exact point distribution will be communicated through the online learning platform.
The project(s) will be evaluated during the year. They have to be made individually and plagiarism will be sanctioned.
The exam is a written exam consisting of multiple-choice questions, fill-in questions and open questions. The exam consists of two parts: the part on the lecture material will test for factual and synthetic knowledge; the part on the exercises will test your ability to solve exercises involving the lecture material, as practiced in the exercise sessions.