@inproceedings {icaps16-77,
    track    = {​Main Track},
    title    = {Search Portfolio with Sharing},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13126},
    author   = {Sandip Aine and  Maxim Likhachev},
    abstract = {Over the years, a number of search algorithms have been proposed in AI literature, ranging from best-first to depth-first searches, from incomplete to optimal searches, from linear memory to unbounded memory searches; each having their strengths and weaknesses. The variability in performance of these algorithms makes algorithm selection a hard problem, especially for performance critical domains. Algorithm portfolios alleviate this problem by simultaneously running multiple algorithms to solve a given problem instance, exploiting their diversity. In general, the portfolio methods do not share information among candidate algorithms. Our work is based on the observation that if the algorithms within a portfolio can share information, it may significantly enhance the performance, as one algorithm can now utilize partial results computed by other algorithms. To this end, we introduce a new search framework, called Search Portfolio with Sharing},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13126 (SP-S), which uses multiple search algorithms to explore a given state-space in an integrated manner, seamlessly combining partial solutions while preserving the constraints/characteristics of the candidate algorithms. In addition, SP-S can be easily adopted to guarantee theoretical properties like completeness, bounded sub-optimality, and bounded re-expansions.  We describe the basics of the SP-S framework and explain how different classes of search algorithms can be integrated in SP-S. We discuss its theoretical properties and present experimental results for multiple domains demonstrating the utility of such a shared approach.},
    keywords = {Search techniques}
}

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