Review: Symbolic Heuristic Search for Factored Markov Decision Processes

From: Jessica Kristan Miller (jessica_at_cs.washington.edu)
Date: Wed Nov 19 2003 - 00:40:47 PST

  • Next message: Keith Noah Snavely: "Review: "Symbolic Heuristic Search for Factored Markov Decision Processes""

    Paper Reviewed: Symbolic Heuristic Search for Factored Markov Decision
                    Processes
    Paper Author: Feng, Hansen
    Reviewed By: Jessica Miller

    Summary:

    This paper combines state space pruning and heuristic search to improve
    the SPUDD algorithm for solving MDPs of very large state spaces
    efficiently.

    Two Important Most Ideas:

    (1) One clear innovative idea in this paper is the use of a heuristic
    search with state abstraction to solve MDPs. The authors have developed a
    version of the LAO* algorithm using ADDs to represent sets of states and
    the other elements of the MDP. Using this heuristic search the authors
    focus the computation on the parts of the state space that are reachable
    from the starting state. Pruning the state space in this way makes for a
    more efficient algorithm to solve large MDPs.

    (2) Another idea that the authors present (although it may have been
    investigated before) is the use of dynamic programming to create an
    admissible heuristic. Perhaps this is only a good idea for MDPs due to
    the fact that iteratively better value functions are generated and can be
    used as admissible heuristics, but it'd be interesting to see if you could
    automatically generate admissible heuristics for other AI search problems.

    Largest Flaw in the Paper:

    There are two things that bother me about this paper. First of all they
    are very vague about the a1-a4 examples. They give very little
    description about what these examples entail so we as the readers have to
    just trust their judgment. The other thing that bothers me is the fact
    that they don't test their improvements on any sort of real world
    problems. So, my main complaints are about the way they conducted their
    experiments.

    Two Open Research Questions:

    (1) This paper shows how getting the right combination of ideas together
    can result in a better algorithm. The authors mention that further
    research into integrating additional "strategies" might yield in even
    better results for solving MDPs.

    (2) A tangential open research idea is the one I mentioned above
    concerning automatically generating admissible heuristics for other
    domains.


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