15-887 Planning, Execution, and Learning
12 units, Carnegie Mellon University
Please refer to httpswww.cs.cmu.educsdgradcourseschedulef16.html for the most recent schedule updates. Summary 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 planning processes. 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 execution. Specific planning techniques to be covered include meansends analysis, linear and nonlinear planning, partialorder planning, graphbased planning, heuristic planning, BBDbased 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, multiagent planning, and scheduling. There are no explicit prerequisites, but a basic knowledge of AI is assumed.
Prerequisites: none listed
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