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We consider the problem of identifying among many candidates a single best solution which jointly maximizes several domain-specific target functions. Assuming that the candidate solutions can be generated incrementally, we model the error in prediction due to the incompleteness of partial solutions as a normally distributed random variable. Using this model, we derive a probabilistic search algorithm that aims at finding the best solution without the necessity to complete and rank all candidate solutions. We do not assume a Viterbi-type decoding, allowing a wider range of target functions.  We evaluate the proposed algorithm on the problem of best parse identification, combining simple heuristic with more complex machine-learning based target functions. We show that the search algorithm is capable of identifying candidates with a very high score without completing a significant proportion of the candidate solutions.

A probabilistic search for the best solution among partially completed candidates