Models of Computation

Course goals

Aim of the course: Students

• learn about and reflect on logic and computation and the relevance of these areas for the theory of artificial intelligence;
• understand the use of models of computation for the foundation of artificial intelligence;
• learn how to read scientific papers and to formulate research questions about them;
• learn to give a presentation;
• learn to write a paper on conducted research;
• learn to give constructive feedback to peers.
  
   

 

Content

Computation is one of the key concepts in Artificial Intelligence. It plays an important role in the foundations of AI, in its theory and its practice. The computational aspects are in fact an important factor in the modeling of any phenomenon in the field of AI. Many approaches in AI use logic, in many different guises, be it as a model, a computational tool, or otherwise. In this way logic and computation are closely related in AI research.

The course has three components. The first one consists of a series of lectures in which the most important models of computation are treated, Turing Machines, Lambda-Calculus and Predicate Logic. In these lectures, the role of logic and formalization in these abstract models is highlighted. These lectures end with a short test.

The second component consists of the following tasks: each student studies two research papers, formulates a research question, gives a presentation about one or both of the papers, and writes a final paper in which both papers and the research question are discussed.
Both papers have to be related to logic and computation and the research question has to concern both topics. There is a collection of papers that all students get access to and where they can look for suitable papers. The collection includes papers on theory as well as applications, and papers on technical as well as philosophical topics. Possible subjects are: logical complexity, explainable AI, quantum computation, automated theorem proving, symbolic versus subsymbolic methods, temporal logic and software verification, and extensions of the Church-Turing Thesis. More information in Brightspace
The third component consists of refereeing tasks carried out by the students. Each student forms a referee pair with a fellow student and has to comment on the drafts of the presentation and final paper of that fellow student in Brightspace.


This course is for students in Artificical Intelligence, as well as students in History and Philosophy of Science and the RMA Philosophy. Students of other MA-programmes, please contact the Course Coordinator. Students History and Philosophy of Science and Artificial Intelligence experiencing problems with enrollment, please contact the Student Desk Humanities, studentdesk.hum@uu.nl

Additional information

Students in History and Philosophy of Science and Artificial Intelligence who experience problems with enrolment, please contact the Student Desk Humanities, studentdesk.hum@uu.nl.
Please note: the time slot shown here is not yet final and may still be modified until the third Wednesday in September.