Artificial IntelligenceFDA006, 2007HT
Doctoral students in computer science.
The course was last given
The course is organized under the assumption that your goal, as a participant, will be to acquire a broad and systematic knowledge and understanding of modern artificial intelligence. "Systematic" means that existing cross-connections within the topic should be understood.
Introductory course in artificial intelligence
Search and optimization: Heuristic, local and adversarial search techniques.
Planning and Scheduling:
- Partial-order planning, hierarchical planning, graph
planning, heuristic planning, domain-dependent planning.
- Planning under uncertainty, planning with time and resources.
- Combining planning and scheduling
Learning: Inductive learning, decision trees, classification, PAC learning, reinforcement learning, learning rules.
Uncertainty reasoning systems
- Belief networks, decision making under uncertainty, acting under uncertainty
- Uncertain reasoning: fuzzy logic, probabilistic logic.
Decision Making: Utility Theory, beliefs and desires, applications in robotics and on the Internet.
Software issues in robotics): Behavior-based robotics, deliberative/reactive architectures, sensing and perceiving, navigation.
Knowledge-based expert systems and applications: Diagnosis/classification, monitoring and control
- Production systems, blackboard architectures, SOAR, belief revision and truth maintenance systems, logic-based systems, multi-agent architectures.
- Model-based diagnosis and execution monitoring.
The course is given in an intensive format ("crash course") at a conference facility
Stuart Russel and Peter Norvig. "Artificial Intelligence - A Modern Approach", Prentice Hall Series in Artifical Intelligence.
Exam and assignment
4,5 hp (3) credits
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03