Knowledge-based Systems and Artificial Intelligence
Reference EHPOOL00000245
Taught in Elective Course List for Master of Computer Science EngineeringMajor Course List for Master of Industrial Engineering and Operations ResearchElective Course List for Master of Biomedical Engineering
Theory (A) 30.0
Exercises (B) 22.5
Training and projects (C) 0.0
Studytime (D) 180.0
Studypoints (E) 6
Credit contract? Access is determined after successful competences assessment
Examination contract? Access is determined after successful competences assessment
Credit contract mandatory if Exam contract? Course included in exam contract
Retake possible? Yes
Teaching Language Dutch
Lecturer Johannes D'Haeyer
Department TW07
Key Words

search, knowledge representation, rule based systems, contraint programming, machine learning

Position of the Course

To let the student gain insight into knowledge intensive problem solving. The focus is on rule-based programming and its application in expert systems.


  • Search: Introduction: search and knowledge representation, Graph search, Constraint Programming
  • Knowledge representation and inference: First Order Logic, Semantic networks, Rulebased Systems, Planning
  • Uncertainty: Bayesian Networks, Fuzzy Logic, Truth maintenance
  • Machine Learning: Decision Tree Learning, Inductive inference, Artificial Neural Networks

Starting Competences

Principles of predicate logic and probability theory

Final Competences

CONCEPTS: knowledge representation; blind and heuristic search; control strategies; restriction-propagation; deduction; monotone and non-monotone logic; semantic networks; rule based inferencing; belief networks; fuzzy logic; assumptions and justifications; induction and machine learning; planning of action

INSIGHTS: declarative modeling; key features of representation formalisms; relation between knowledge representation and search; limitations of search; exploiting knowledge in search strategies; control principles applied in problem solvers; complexity of deductive reasoning; efficient organisation of knowledge; representations and associated inference mechanisms; reasoning with uncertain information and evidence propagation; reasoning with fuzzy information; reasoning with assumptions and justifications; modeling causal relationships and independence; complexity of inductive reasoning

SKILLS: being able to analyse the performance of search strategies; being able to structure and represent knowledge using an appropriate representation formalism; learn to work with a rule based programming environment; learn to apply induction algorithms to extract knowledge from data (data mining)

Teaching and Learning Material

Cost: 10.0 EUR Syllabus (in Dutch) and exercises: CLIPS (the C language integraded production system)


  • S. Russel, P. Norvig, Artificial Intelligence, A Modern Approach, Second Edition, Prentice Hall (2003)
  • M. Ginsberg, Essentials of Artificial Intelligence, Morgan Kaufmann (1993)
  • G.F. Luger, W.A. Stubblefield, Artificial Intelligence, Structures and Strategies for Complex Problem Solving, Addison-Wesley (1998)

Study Coaching

Teaching Methods

Classroom lectures; Classroom problem solving sessions; Computer-assisted problem solving

Evaluation Methods

Evaluation during examination period

Examination Methods

During examination period: written closed-book exam; graded project reports

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