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

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

Principles of predicate logic and probability theory

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)

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)

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

Evaluation during examination period

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