16.410/16.413  Prin of Autonomy & Dec Making

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

Prerequisites: 6.041 and 1.00.
Units: 3-0-9

For Course Learning Objectives and Logistics, please look under Syllabus.

Text Books:
o Primary Text: [AIMA] Artificial Intelligence: A Modern Approach, 2nd Edition, by Stuart Russell and Peter Norvig
o Recommended Text: [IOR] Introduction to Operations Research, by Frederick S. Hillier and Gerald J. Lieberman
o Recommended Text: [PA] Planning Algorithms, by S. LaValle (Cambridge Press), available at http://planning.cs.uiuc.edu/
o Recommended Text: [JINS] Java in a Nutshell, by David Flanagan

AIMA, IOR and JINS available at the Coop and on reserve at the MIT Library.

Instructors:

Brian Williams Rm 33-330, x3-1678 or 32-276, x3-2739

Emilio Frazzoli Rm 33-332, x3-0305 or 32D-724 x3-1991

TA: Julie Shah

Course Administrator: Kathryn Fischer Rm 33-336, x3-6270

Learning Objectives, Course Policy, Grading, and other information can be found here:

stellar.mit.edu/S/course/16/fa09/16.410/courseMaterial/topics/topic2/syllabus/text/text