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Planning as State Space Search

  • This lecture will consider planning as state-space search, for which there are several options:
    • Forward from I (initial state) until G (goal state) is satisfied;
    • Backward from G until I is reached;
    • Use a Heuristic function to estimate G- or I-distance, respectively – prefer states that have a lower heuristic value.

  • It will also introduce partial-order planning as a more flexible approach

  • In all cases there are three implicit concerns to consider:
    • Representation – how the problem/state space and goal is defined;
    • Algorithm – e.g. which (combination) of these approaches is used;
    • Complexity and decidability – the feasibility of the approach.

Speaker notes:

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