A hallmark of recent AI conference papers, journal papers and theses has been the incorporation of ideas from outside traditional AI. Techniques from Probability, Statistics, Economics, Algorithms, Operations Research and Optimal Control are increasingly important tools for improving the intelligence and autonomy of machines, whether those machines are robots surveying Antartica, schedulers moving billions of dollars of inventory, spacecraft deciding which experiments to perform, vehicles negotiating for lanes on the freeway, or data management systems that persistently scan for anomalies and trends. The primary content of this AI course is a review of a selected set of these tools. The course will cover the ideas underlying these tools, their implementation, and how to use them or extend them in your research.
Requirements and target audience. The course is tailored to participants who have already been exposed to some pre-PhD-level training in AI, but provisions are made (by some extra evening "executive briefing" sessions in the early part of the course) for students who are new to the discipline, but who are comfortable with computer science basics such as data structures, programming, basic probability and elementary algorithms. Students who feel they may be lacking these basics should consult the instructor, and may be advised to begin by taking 15-381, the undergraduate AI course.
Syllabus outline: We will travel through a wide range of general scenarios that might be encountered in the design of an intelligent systems such as embedded autonomous controllers, corporate logistics controllers or alarm and diagnosis systems. The scenarios are differentiated by various combinations of assumptions about:
Last updated January 2003 by Vincent Conitzer