|Graph and tree
In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is an optimization of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states) according to some heuristic which attempts to predict how close a partial solution is to a complete solution (goal state). But in beam search, only a predetermined number of best partial solutions are kept as candidates.
Beam search uses breadth-first search to build its search tree. At each level of the tree, it generates all successors of the states at the current level, sorting them in increasing order of heuristic cost. However, it only stores a predetermined number of best states at each level (called the beam width). Only those states are expanded next. The greater the beam width, the fewer states are pruned. With an infinite beam width, no states are pruned and beam search is identical to breadth-first search. The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists). By the nature of best-first search and since beam search is a special case of best-first search, it is not optimal (that is, there is no guarantee that it will find the best solution). It returns the first solution found.
The beam width can either be fixed or variable. One approach that uses a variable beam width starts with the width at a minimum. If no solution is found, the beam is widened and the procedure is repeated.
A beam search is most often used to maintain tractability in large systems with insufficient amount of memory to store the entire search tree. For example, it is used in many machine translation systems. To select the best translation, each part is processed, and many different ways of translating the words appear. The top best translations according to their sentence structures are kept, and the rest are discarded. The translator then evaluates the translations according to a given criteria, choosing the translation which best keeps the goals. The first use of a beam search was in the Harpy Speech Recognition System, CMU 1976.
Beam search has been made complete by combining it with depth-first search, resulting in Beam Stack Search and Depth-First Beam Search, and limited discrepancy search, resulting in Beam Search Using Limited Discrepancy Backtracking (BULB). The resulting search algorithms are anytime algorithms that find good but likely sub-optimal solutions quickly, like beam search, then backtrack and continue to find improved solutions until convergence to an optimal solution.
- beam search from FOLDOC
- British Museum Search
- Norvig, Peter. Paradigms of Artificial Intelligence. http://books.google.com/books?id=X4mhySvjqUAC&pg=PA196&lpg=PA196&dq=beam+search+in+paradigms+of+artificial+intelligence+programming&source=web&ots=20BH2lB3sc&sig=udBIO2Qeg8awaySgwCu7gDEObY4&hl=en&sa=X&oi=book_result&resnum=1&ct=result#PPA196,M1 Programming
- Furcy, David. Koenig, Sven. "Limited Discrepancy Beam Search". 2005. http://www.ijcai.org/papers/0596.pdf
- Tillmann, Christoph. Ney, Hermann. "Word Reordering and a Dynamic Programming Beam Search Algorithm for Statistical Machine Translation". http://acl.ldc.upenn.edu/J/J03/J03-1005.pdf
- Lowerre, Bruce. "The Harpy Speech Recognition System", Ph.D. thesis, Carnegie Mellon University, 1976
- Zhou, Rong. Hansen, Eric. "Beam-Stack Search: Integrating Backtracking with Beam Search". 2005. http://www.aaai.org/Library/ICAPS/2005/icaps05-010.php