Course syllabus (Old version.)

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Description

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.

Course Outline

• 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.