Admission Requirements
Individual JAVA programming assignment: PASS/FAIL, handed out after the first lecture. (entry assignment)
Without passing this assignment, participation is not possible.
Recommended prior knowledge:
Propositional and Predicate Logic, Basic Search and Sorting, Trees, Graphs, States and State spaces.
This means that without having passed Algorithmics and Datastructures and Logic Symbolic AI is a difficult course
Description
Artificial Intelligence is about developing intelligent autonomous decision-making systems. These systems are integrated into things like cars, robots, medical expert systems, and websites. Such systems use a collection of different techniques, such as machine learning, reinforcement learning, as well as logic, planning, and reasoning. In this course, you will learn the basics of classical artificial intelligence. The focus of this course is on understanding and applying by programming various forms of logical decision-making systems, planners, and planning algorithms, and applying them in autonomous agents. These systems often rely on knowledge represented in the form of symbols and logical formulas, over which rules can reason to produce new knowledge or actions. Therefore, we not only cover advanced search techniques but also different forms of symbolic knowledge representation. Hence the term Symbolic AI.
Course Objectives
- Remember and understand classical AI concepts:
Describe different knowledge and state representations commonly used in symbolic AI (including trees, graphs, grids, first order logic, and MDPs), agent types, and environment characteristics.
Explain different types of agents, environments, and states by knowing their features.
Explain how the field of Symbolic AI fits in the field of AI, by comparing the differences in approach between Symbolic AI and Machine Learning.
Explain the impact of knowledge representations on states, reasoning mechanisms, and agent capabilities.
- Understand, apply and analyze Symbolic AI techniques:
Apply and implement basic search and planning algorithms including Iterative deepening, Alfa Beta, Heuristic search, and Breadth-Depth first search applied to Trees and Graphs to a problem.
Apply and implement basic value-based optimization using dynamic programming in a grid world represented as an MDP.
Apply and implement basic logic inference based on first order logic and forward chaining.
Show the capability to analyze different symbolic AI algorithms by looking at their characteristics and features.
- Understand and analyze the relation between code and theory:
Use different languages (Prolog, Python, Java) to implement the above-mentioned algorithms in order to understand the impact of language on the algorithmic solution.
Analyze the behavior of these implementation algorithms by relating the implementation back to the theoretical algorithm.
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of Instruction
Lectures: Live lectures to teach theoretical concepts and examples of classic AI algorithms. Some pre-recorded (old) videos are avilable but this is only to help give some additional material, not a replacement of the live lectures.
Lab: On-site: lab rooms in new gorleaus. During labs students hand in their assignments live (see below assessement). Further, during lab hours students can ask clarifying questions about the lab assignments, exercises, and course material.
Assessment Method
-Assignments (50%): approx. 10 group assignments (groups of 2 students), to be handed in and explained live during the lab sessions. Overall grade for assignments is the weighted average of the individual assignment grades. Assignments need to be handed in before the assignment deadline. Failure to do so without a valid reason results in failing the assignment (and therefore the course, see below). There is no retake possibility for an assignments after the deadline passed. Before the assignment deadline, an assignment may be handed in as often as needed to pass.
-One theoretical exam (50%): the exam focusses on remembering and understanding theoretical classical AI concepts, and understanding and analysing algorithms and calculation exercises with logic, utility and MDPs. There is one resit for the exam in the same academic year.
The instructor will inform the students about the review and debriefing of the exam.
To pass the course, the exam AND the average assignment grades must be >=5. The overall average must be >=5.5
Reading List
Russel & Norvig "Artificial Intelligence: A Modern Approach". (book)
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Education coordinator LIACS bachelors
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.