Fall 2021 - CMPT 417 D100
Intelligent Systems (3)
Class Number: 4706
Delivery Method: In Person
Course Times + Location:
Mo, We, Fr 1:30 PM – 2:20 PM
WMC 3520, Burnaby
Exam Times + Location:
Dec 17, 2021
3:30 PM – 6:30 PM
AQ 3181, Burnaby
Prerequisites:CMPT 225 with a minimum grade of C-.
Intelligent Systems using modern constraint programming and heuristic search methods. A survey of this rapidly advancing technology as applied to scheduling, planning, design and configuration. An introduction to constraint programming, heuristic search, constructive (backtrack) search, iterative improvement (local) search, mixed-initiative systems and combinatorial optimization.
Many real computing applications involve computational problems for which no generally efficient algorithms are known. These problems often involve relatively small inputs, but astronomically-sized search spaces in which solutions may be well hidden. Developing practical software for solving these problems is challenging and costly. This course examines methods for automatically solving these problems based on high-level specifications, much as we use a query language rather than low-level algorithms to answer complex questions about a large data set. The course will pursue two tracks in parallel. In one, we will examine the design and use of real systems that are products of major companies or widely used research tools, including hands-on experience. In the other, we will develop formal foundations to support principled design and analysis of such systems and their use in critical applications, and to understand their strengths and limitations. We will begin with lectures on fundamentals, and toward the end shift to considering current issues and research directions. The course is suitable for students with an interest in research or in tackling problems that are not well solved. A strong discrete math and algorithm background is recommended. Ideally, students should have taken CMPT-307 and CMPT-310, or at least have MACM-201. The individual project requires knowledge of Python programming.
COURSE-LEVEL EDUCATIONAL GOALS:
- Intractable AI and optimization problems
- Improved versions of best-first search, bounded-suboptimal search, local search, etc.
- Advanced techniques: Generating complex heuristics, bidirectional and dual searches, etc.
- A case study of the multi-agent pathfinding problem
- Applications in planning, multi-agent/robot systems, games, etc.
Individual Project (20%), Late Midterm Exam (30%), Final Project (50%).
- Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russell and Peter Norvig, Pearson/Prentice Hall,
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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TEACHING AT SFU IN FALL 2021
Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place. Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.