COMP-765: Planning Algorithms
(Course not offered in 2010-2011.)
Course syllabus (Old version.)
This course will cover a broad spectrum of planning
algorithms, including motion planning, discrete planning,
planning under uncertainty, and decision-theoretic planning
The course will be relevant to researcher in robotics, AI,
algorithms, computational geometry and computer graphics
The course will be a mix of lectures, discussions and student
presentations. Students will be evaluated based on their class
participation, demonstrated ability to read and understand technical
papers, assignments, and a final project.
- Introduction. Overview. Goals.
- Discrete planning
- State-space search. STRIPS planning.
- Plan-space search. Partial-order planning.
- Planning as satisfiability.
- Heuristic planning.
- Motion planning.
- Geometric representations and transformations.
- Configuration space.
- Sampling-based motion planning.
- Combinatorial motion planning. Algebraic cell decomposition. Voronoi diagrams.
- Decision-theoretic planning
- Utility theory. Decision theory.
- Sequential decision theory. Markov Decision Processes. Reinforcement learning.
- Sensors and information spaces. Discrete, parametric and sample-based information spaces.
- Planning under sensor uncertainty. Partially observable Markov decision processes.