Agent centered search

From: MAUSAM (mausam_at_cs.washington.edu)
Date: Tue May 13 2003 - 10:51:47 PDT

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    Agent-Centered Search
    Sven Koenig

    This paper illustrates a review of planning problems where real
    time algorithms (interleaving planning and execution) can be used.

    The paper calls such algorithms as agent centered search because the
    execution and planning are done by an intelligent agent. The most distinct
    advantage of such an agent centered search is that one could aim at
    achieving minimization of sum of planning and execution cost, thereby
    minimizing the total time taken by the agent to reach the goal. However,
    this approach might lead to troubles especially if there are some
    irreversible actions as the agent will execute some action without being
    too confident about it.

    The author presents his LRTA* algorithm, which performs a local search
    updating the value function for the local space, then computes the best
    action from the current state, executes the action and repeats the
    process. The author mentions that most of the other algorithms that do
    real time planning fall into some version of LRTA*, with some changes. So,
    LRTA* in some sense unifies the domain of real time planning. Then the
    author goes on to give various examples of different domains where such
    methods work.

    The major drawback of the paper is that the paper talks about a lot of
    things but never gives any experimental justifications for any of it. The
    author discusses various examples but does not compare his algorithm with
    any other existing one. For cases where interleaving would have have
    deteriorated the quality of results, the extent of that could have been
    mentioned as experiments. Similarly, the speed up using minimization of
    total time could be compared to classical planning approaches.

    For future work, I couldn't exactly follow how does the agent decides that
    in the step, it has done sufficient planning to do an action. Likewise
    how does the agent determine what the local search space would be
    considered as. I don't know if these questions have already been studied
    but if they are not they should be the first things to be studied to make
    LRTA* a practical solution.


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