On the Optimality of Randomized $\alpha$-$\beta$ Search
SIAM Journal on Computing
Randomized algorithms
Optimization techniques for queries with expensive methods
ACM Transactions on Database Systems (TODS)
Online computation and competitive analysis
Online computation and competitive analysis
Optimal Search on Some Game Trees
Journal of the ACM (JACM)
The solution for the branching factor of the alpha-beta pruning algorithm and its optimality
Communications of the ACM
Processing Queries with Expensive Functions and Large Objects in Distributed Mediator Systems
Proceedings of the 17th International Conference on Data Engineering
Query Optimization in the Presence of Foreign Functions
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Randomized Approximation Algorithms for Query Optimization Problems on Two Processors
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Query strategies for priced information
Journal of Computer and System Sciences - Special issue on STOC 2000
Probabilistic Boolean decision trees and the complexity of evaluating game trees
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
| Hi-index | 5.23 |
Recently, Charikar et al. investigated the problem of evaluating AND/OR trees, with non-uniform costs on its leaves, from the perspective of the competitive analysis. For an AND/OR tree T they presented a @m(T)-competitive deterministic polynomial time algorithm, where @m(T) is the number of leaves that must be read, in the worst case, in order to determine the value of T. Furthermore, they proved that @m(T) is a lower bound on the deterministic competitiveness, which assures the optimality of their algorithm. The power of randomization in this context has remained as an open question. Here, we take a step towards solving this problem by presenting a 56@m(T)-competitive randomized polynomial time algorithm. This contrasts with the best known lower bound @m(T)/2.