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Planning via Random Walk-Driven Local Search

Planning via Random Walk-Driven Local Search. Fan Xie Hootan Nakhost Martin Müller Presented by: Hootan Nakhost. Outline. Random Walks Planning Problems of Random Walks Random Walk-Driven Local Search Experiments Conclusion and Future Works. Arvand Planner [ Nakhost and Müller 2009].

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Planning via Random Walk-Driven Local Search

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  1. Planning via Random Walk-Driven Local Search Fan Xie HootanNakhost Martin Müller Presented by: HootanNakhost

  2. Outline • Random Walks Planning • Problems of Random Walks • Random Walk-Driven Local Search • Experiments • Conclusion and Future Works

  3. Arvand Planner[Nakhostand Müller 2009] • Exploration using random walks to overcome the problem of local minima and plateaus. • Jumping greedily exploits the knowledge gained by the random walks.

  4. Aras: Plan Improving Postprocessor[Nakhostet al. 2011] • Expand a neighbor search space along the input plan. • Output the shortest path in the neighbor search space

  5. Problems of Random Walks • Fails in Narrow Exit Path Search Space (we will explain later) • Poor Plan Quality

  6. Narrow Exit Path Search Space

  7. Narrow Exit Path Search Space(2)

  8. Plan Quality

  9. Random Walk-Driven Local Search • Local Greedy Best First search (local GBFS) • Perform one random walk from the node going to be expanded • Keep the best random walk (lowest h) • Jump to the best state (in GBFS or end-point of random walk)

  10. Random Walk-Driven Local Search For every node in the open list, it has two heuristic value: hn: the heuristic value of the node itself hr: the heuristic value of the end-point of the random walk starting from the node Nodes in the open list are ordered by a linear combination of hn and hr(W = 100 in our experiments): W * hn+ hr

  11. Analysis of RW-LS • Advantage: • A small local search can help escape some small Narrow-Exit-Path-Search-Space • Generally, generates better solutions • Disadvantage: • Slow down speed

  12. Coverage Results of IPC-2011 Benchmark

  13. Quality Results of IPC-2011 Benchmark

  14. Larger Problems Easy domains with scalable generators are scaled to get larger problems: The number in parentheses are the max number used in the IPC-2011 benchmark

  15. Coverage Results of Large Problems

  16. Quality Results of Large Problems

  17. Conclusion and Future Work • Contribution: • RW-LS: A strong algorithm combining local search and random walks, implemented in the Planner Arvand-LS. • Motivated and Developed larger problems • Future work: • Make it a global search algorithm • Make it one entry in a portfolio planner • Add multi-heuristic into the algorithm

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