15-887*: Planning, Execution, and Learning
MW 1:30-2:50pm, GHC 5222
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 find "good" or "optimal"
plans,how to represent time, and how to dynamically combine planning and
Specific planning techniques to be covered include: means-ends analysis,
linear and non-linear planning, partial-order planning, graph-based
planning, heuristic planning, BBD-based planning, hierarchical planning,
temporal planning, conditional and conformant planning, probabilistic
planning and learning using Markov models (MDPs and POMDPs), integration of
planning, perception and execution, execution monitoring and replanning,
path planning, multi-agent planning, and scheduling.
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), a project (25%), and a take-home final exam (30%).
- Schedule, Course Notes, and Readings :
- contains the current class schedule, pointers to materials
presented in the lectures, notes, and pointers to readings.
- Homeworks and Project:
- contains information about each assignment, as it becomes available.
- Final Exam
- You will be able to take the exam over any 24 hour period during the finals week.
July 23, 2016