| TDDC51 | Artificial Intelligence, 4,5 ECTS credits. /Artificial Intelligence/ |
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OBS! |
Only open for students admitted to the Computer Science Master programme |
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Prel. scheduled
hours: |
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Area of Education: Technology Subject area: Computer Science, Computer Engineering |
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| Advancement level
(G1, G2, A): A
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| Aim: 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. |
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| Prerequisites: (valid for students admitted to programmes within which the course is offered) Introductory course in artificial intelligence Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding. |
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| Organisation: Lectures. The course is given in an intensive format ("crash course") at a conference facility. |
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| Course contents: 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. Neural networks 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 Evolutionary computing AI architectures - Production systems, blackboard architectures, SOAR, belief revision and truth maintenance systems, logic-based systems, multi-agent architectures. - Model-based diagnosis and execution monitoring. |
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| Course literature: Stuart Russel and Peter Norvig. "Artificial Intelligence - A Modern Approach", Prentice Hall Series in Artifical Intelligence. |
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| Examination: | ||||
| Written examination. |
3 p |
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4,5 ECTS |
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| Grades given are Pass, Fail. | ||||
Course language is English. Department offering the course: IDA. Director of Studies: Examiner: Erik Sandewall Link to the course homepage at the department Course Syllabus in Swedish |
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