Aims

The course aims

• to provide a thorough introduction and overview of the knowledge and the techniques that form the basis of systems developed in Artificial Intelligence,
• to take a general perspective on the domain, with a focus mostly on problem solving techniques,
• to provide a workbench of different problem solving techniques developed in this context, but restricting to those that are not presented in other mandarory courses of the MAI program.

Previous knowledge

Knowledge of some programming language. Some familiarity with algorithms and data structures.

Content

1. Introduction (2 u.)
- Definition and general context, both of the domain and the course
 
2. State-space representation and search methods (8 u.)
- state-space representation: introduction and trade-offs,
- blind search,
- heuristic search, including the study of the A*-algorithm,
- advanced aspects of heuristic search,
- heuristic search in games
 3. Machine Learning (2 u.)
- Version Spaces as an illustration of Machine Learning
 
4. Constraint propagation (3 u.)
- backtracking, backtrack variants, intelligent and dependency directed backtracking,
- arc consistency techniques,
- hybrid constraint propagation methods
 5. Some case studies of the use of constraint processing (3 u.)
- interpretation of line drawings,
- interpretatie of natural language
 6. Planning and Temporal representation (1.5 u.)
- partial-order regression planning: STRIPS
- situation calculus and deductive planning
7. Automated reasoning.
- first order logic, syntax and semantics
- normalisation, unification
- resolution (both linear and general resolution)
- logic programming
The themes of the course are linked to current
research where possible.

Course material

Articles and literature
Slides, transparencies, courseware
Multimedia

Is also included in other courses

• Master of Artificial Intelligence (Option: Engineering and Computer Science (ECS)) 60 ects.