CUGS CS Review / CUGS CS Core / CUGS CS Advanced
Once a year
Suggested # of Credits
4.5 HE Credits (Higher Education Credits)
Doctoral students in computer science.
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
Planning and Scheduling:
Partial-order planning, hierarchical planning, graph
planning, heuristic planning, domain-dependent planning.
Planning under uncertainty, planning with time and
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
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,
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,
Model-based diagnosis and execution monitoring.
Department of Computer Science, Linköping University
See course schedule.
Exam and assignment
Stuart Russel and Peter Norvig. "Artificial Intelligence - A Modern
Approach", Prentice Hall Series in Artifical Intelligence.
Russell & Norvig textbook webpage can be found here.
Detailed reading list:
Chapters 12-17, 19-21, 24-25 + Lectures notes
The course is given in an intensive format ("crash course") at a