Symbolic Systems 1

Course manual 2019/2020

Course content

Symbolic systems are used in fields of AI that are concerned with problem solving and search, knowledge representation and reasoning, among many other fields. In this course we present a selection of these symbolic systems and show how they can be used to solve various tasks in AI. We will begin by investigating the domain of problem solving and search, looking at several illustrative examples of search problems in AI, discussing various approaches that are available for problem solving and search (including SAT solvers, constraint programming, and answer set programming). We then move on to discuss the field of automated planning, investigate various settings where automated planning is relevant, and study some approaches available for solving planning problems. Finally, we will study the problem of representing knowledge and reasoning with it using ontologies and description logics, and we will have a look at the Web Ontology Language (OWL).

Study materials

Literature

• Stuart Russell, and Peter Norvig. Artificial Intelligence: A Modern Approach (3rd Ed.), Prentice Hall, 2016.

• Frank van Harmelen, Vladimir Lifschitz, and Bruce Porter (Eds.). Handbook of Knowledge Representation, Elsevier, 2008.

• Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. Answer Set Solving in Practice, Morgan & Claypool, 2012.

Syllabus

• Online syllabus

Practical training material

• Exercises

Software

• Clingo

• PySAT

• Gurobi

• OR-Tools

Other

• Descriptions of how to use the software

Objectives

• Student is able to understand and construct a problem encoding in ASP
• Student understands how grounding in ASP works
• Student understands answer set semantics of logic programs, and is able to derive answer sets
• Student understands and is able to detect non-monotonicity in reasoning
• Student understands the syntax and semantics of default logic
• Student understands formal planning frameworks, and can use it to model planning problems
• Student understands combinatorial explosion present in planning problems
• Student understands the syntax and semantics of the basic description logic ALC

Teaching methods

• Lecture
• Self-study

During the lectures, students learn the theoretical basis of the material that is covered in the course, and they learn about the connection to the larger research field in which the topic is embedded. During self-study, the students get hands-on experience with using the different (programming) techniques, and they deepen their knowledge of the theoretical aspects of the topic.

Learning activities

Attendance

This programme does not have requirements concerning attendance (OER part B).

Additional requirements for this course:

Attendance is not mandatory.

Assessment

The partial grade 'homework assignments' is based on four homework assignments, and this partial grade is the average of the grades for the homework assignments. This partial grade does not have a resit.

The partial grade 'tentamen' is based on a final exam. This is a digital (online) exam, using the software ANS. This is an open-book and open-notes exam. The exam covers all the material. There is no cut-off score for this exam (i.e., the cut-off is only on the final grade). This partial grade does have a resit—in the form of a resit exam (which is entirely similar to the original exam in scope and set-up).

The final grade is the average of the two partial grades. The cut-off for passing is 5.5.

Inspection of assessed work

The grades and feedback for the homework assignments are reported via Canvas. Students can thus in this way inspect their assessed work and the assessment models. This opportunity is announced via Canvas.

The grades and feedback for the final exam are reported via ANS. Students can thus in this way inspect their assessed work and the assessment models. This opportunity is announced via Canvas.

Additionally, students can pose questions about their assessment in the online discussion boards, and there is space for this during the first question-and-answering session after the posting of the grades and feedback.

Assignments

There are four assessed homework assignments: two theoretical assignments and two programming assignments.

1. Solving Sudoku's using the four programming paradigms (SAT, ASP, CSP, ILP)—programming assignment.
2. Theoretical homework about SAT and ASP.
3. Solving planning problems using ASP—programming assignment.
4. Theoretical homework about OWL and description logics.

The homework assignments have to be made individually and are graded. Feedback will be given via Canvas and the Codegrade Canvas plugin.

In addition, there are weekly (non-graded) quizzes on Canvas about the reading material. After making these quizzes, feedback is provided automatically by Canvas.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Contact information

Coordinator

• dr. Ronald de Haan

Staff

• Dr. Ronald de Haan (lecturer)
• Lorian Coltof BSc (teaching assistant)
• Boas Kluiving BSc (teaching assistant)
• D.C.V. Louwrink (teaching assistant)
• Adriaan de Vries BSc (teaching assistant)