15-887A: AI Planning, Execution, and Learning
Mondays and Wednesdays, 3:00-4:20
Planning is ubiquitous in everyday life --
from planning how to make dinner to planning how to graduate from
University with the least amount of work. Researchers in AI have studied
planning problems for many years, and many techniques exist for automating
This course will explore both classical and modern approaches to
planning. Issues to be discussed include: how to represent actions and
world state, how to search for plans efficiently, how to deal with
uncertainty in actions and the world state, how to represent time, and how
to dynamically combine planning and execution.
Specific planning techniques to be covered include: means-ends analysis,
linear and non-linear planning, partial-order planning, heuristic planning,
GraphPlan, SatPlan, OBBD-based planning, hierarchical planning,
conditional planning, probabilistic planning and learning using Markov models (MDPs and
POMDPs), integration of planning, perception and execution, execution
monitoring and replanning, and robot (geometric)
There are no explicit prerequisites, but a basic knowledge of AI is assumed.
This is a lecture course. There is no textbook, but students will study
research papers and use existing planners. There will be three homeworks
(15% each towards final grade), a term project (30%) and a final exam (25%).
reids -at- cs.cmu.edu
- contains a list of topics to be studied.
- contains the current class schedule.
- lists the course readings and contains links to electronic versions of some of
- contains information about each assignment, as it becomes available.
- Course notes:
- contains material presented in the lectures.
September 9, 2001