The Experts below are selected from a list of 72792 Experts worldwide ranked by ideXlab platform
Ouri Wolfson - One of the best experts on this subject based on the ideXlab platform.
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a Competitive Dynamic data replication algorithm
International Conference on Data Engineering, 1993Co-Authors: Yixiu Huang, Ouri WolfsonAbstract:A distributed algorithm for Dynamic data replication of an object in a distributed system is presented. The algorithm changes the number of replicas and their location in the distributed system to optimize the amount of communication. The algorithm Dynamically adapts the replication scheme of an object to the pattern of read-write requests in the distributed system. It is shown that the cost of the algorithm is within a constant factor of the lower bound. >
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ICDE - A Competitive Dynamic data replication algorithm
Proceedings of IEEE 9th International Conference on Data Engineering, 1Co-Authors: Yixiu Huang, Ouri WolfsonAbstract:A distributed algorithm for Dynamic data replication of an object in a distributed system is presented. The algorithm changes the number of replicas and their location in the distributed system to optimize the amount of communication. The algorithm Dynamically adapts the replication scheme of an object to the pattern of read-write requests in the distributed system. It is shown that the cost of the algorithm is within a constant factor of the lower bound. >
Daniel D Sleator - One of the best experts on this subject based on the ideXlab platform.
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o log log n Competitive Dynamic binary search trees
Symposium on Discrete Algorithms, 2006Co-Authors: Chengwen Chris Wang, Jonathan Derryberry, Daniel D SleatorAbstract:The Dynamic Optimality Conjecture [ST85] states that splay trees are Competitive (within a constant Competitive factor) among the class of all binary search tree (BST) algorithms. Despite 20 years of research this conjecture is still unresolved. Recently, Demaine et al. [DHIP04] suggested searching for alternative algorithms which have small but non-constant Competitive factors. They proposed Tango, a BST algorithm which is nearly Dynamically optimal - its Competitive ratio is O(log log n) instead of a constant. Unfortunately, for many access patterns, such as random and sequential, Tango is worse than other BST algorithms by a factor of log log n.In this paper, we introduce the multi-splay tree (MST) data structure, which is the first O(log log n)-Competitive BST to simultaneously achieve O(log n) amortized cost and O(log2 n) worst-case cost per query. We also prove the sequential access lemma for MSTs, which states that sequentially accessing all keys takes linear time. Thus, MSTs are O(log log n)-Competitive like Tango but, unlike Tango, require only O(log n) amortized time per access in an arbitrary sequence and only O(1) amortized time per access during a sequential access sequence.Furthermore, we generalize the standard framework for Competitive analysis of BST algorithms to include updates (insertions and deletions) in addition to queries. In doing so, we extend the lower bound of Wilber [Wil89] and Demaine et al. [DHIP04] to handle these update operations. We show how MSTs can be modified to support these update operations and be O(log log n)-Competitive in the new framework while maintaining the rest of the properties above.
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SODA - O (log log n )-Competitive Dynamic binary search trees
Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm - SODA '06, 2006Co-Authors: Chengwen Chris Wang, Jonathan Derryberry, Daniel D SleatorAbstract:The Dynamic Optimality Conjecture [ST85] states that splay trees are Competitive (within a constant Competitive factor) among the class of all binary search tree (BST) algorithms. Despite 20 years of research this conjecture is still unresolved. Recently, Demaine et al. [DHIP04] suggested searching for alternative algorithms which have small but non-constant Competitive factors. They proposed Tango, a BST algorithm which is nearly Dynamically optimal - its Competitive ratio is O(log log n) instead of a constant. Unfortunately, for many access patterns, such as random and sequential, Tango is worse than other BST algorithms by a factor of log log n.In this paper, we introduce the multi-splay tree (MST) data structure, which is the first O(log log n)-Competitive BST to simultaneously achieve O(log n) amortized cost and O(log2 n) worst-case cost per query. We also prove the sequential access lemma for MSTs, which states that sequentially accessing all keys takes linear time. Thus, MSTs are O(log log n)-Competitive like Tango but, unlike Tango, require only O(log n) amortized time per access in an arbitrary sequence and only O(1) amortized time per access during a sequential access sequence.Furthermore, we generalize the standard framework for Competitive analysis of BST algorithms to include updates (insertions and deletions) in addition to queries. In doing so, we extend the lower bound of Wilber [Wil89] and Demaine et al. [DHIP04] to handle these update operations. We show how MSTs can be modified to support these update operations and be O(log log n)-Competitive in the new framework while maintaining the rest of the properties above.
Yixiu Huang - One of the best experts on this subject based on the ideXlab platform.
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a Competitive Dynamic data replication algorithm
International Conference on Data Engineering, 1993Co-Authors: Yixiu Huang, Ouri WolfsonAbstract:A distributed algorithm for Dynamic data replication of an object in a distributed system is presented. The algorithm changes the number of replicas and their location in the distributed system to optimize the amount of communication. The algorithm Dynamically adapts the replication scheme of an object to the pattern of read-write requests in the distributed system. It is shown that the cost of the algorithm is within a constant factor of the lower bound. >
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ICDE - A Competitive Dynamic data replication algorithm
Proceedings of IEEE 9th International Conference on Data Engineering, 1Co-Authors: Yixiu Huang, Ouri WolfsonAbstract:A distributed algorithm for Dynamic data replication of an object in a distributed system is presented. The algorithm changes the number of replicas and their location in the distributed system to optimize the amount of communication. The algorithm Dynamically adapts the replication scheme of an object to the pattern of read-write requests in the distributed system. It is shown that the cost of the algorithm is within a constant factor of the lower bound. >
Chengwen Chris Wang - One of the best experts on this subject based on the ideXlab platform.
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o log log n Competitive Dynamic binary search trees
Symposium on Discrete Algorithms, 2006Co-Authors: Chengwen Chris Wang, Jonathan Derryberry, Daniel D SleatorAbstract:The Dynamic Optimality Conjecture [ST85] states that splay trees are Competitive (within a constant Competitive factor) among the class of all binary search tree (BST) algorithms. Despite 20 years of research this conjecture is still unresolved. Recently, Demaine et al. [DHIP04] suggested searching for alternative algorithms which have small but non-constant Competitive factors. They proposed Tango, a BST algorithm which is nearly Dynamically optimal - its Competitive ratio is O(log log n) instead of a constant. Unfortunately, for many access patterns, such as random and sequential, Tango is worse than other BST algorithms by a factor of log log n.In this paper, we introduce the multi-splay tree (MST) data structure, which is the first O(log log n)-Competitive BST to simultaneously achieve O(log n) amortized cost and O(log2 n) worst-case cost per query. We also prove the sequential access lemma for MSTs, which states that sequentially accessing all keys takes linear time. Thus, MSTs are O(log log n)-Competitive like Tango but, unlike Tango, require only O(log n) amortized time per access in an arbitrary sequence and only O(1) amortized time per access during a sequential access sequence.Furthermore, we generalize the standard framework for Competitive analysis of BST algorithms to include updates (insertions and deletions) in addition to queries. In doing so, we extend the lower bound of Wilber [Wil89] and Demaine et al. [DHIP04] to handle these update operations. We show how MSTs can be modified to support these update operations and be O(log log n)-Competitive in the new framework while maintaining the rest of the properties above.
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SODA - O (log log n )-Competitive Dynamic binary search trees
Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm - SODA '06, 2006Co-Authors: Chengwen Chris Wang, Jonathan Derryberry, Daniel D SleatorAbstract:The Dynamic Optimality Conjecture [ST85] states that splay trees are Competitive (within a constant Competitive factor) among the class of all binary search tree (BST) algorithms. Despite 20 years of research this conjecture is still unresolved. Recently, Demaine et al. [DHIP04] suggested searching for alternative algorithms which have small but non-constant Competitive factors. They proposed Tango, a BST algorithm which is nearly Dynamically optimal - its Competitive ratio is O(log log n) instead of a constant. Unfortunately, for many access patterns, such as random and sequential, Tango is worse than other BST algorithms by a factor of log log n.In this paper, we introduce the multi-splay tree (MST) data structure, which is the first O(log log n)-Competitive BST to simultaneously achieve O(log n) amortized cost and O(log2 n) worst-case cost per query. We also prove the sequential access lemma for MSTs, which states that sequentially accessing all keys takes linear time. Thus, MSTs are O(log log n)-Competitive like Tango but, unlike Tango, require only O(log n) amortized time per access in an arbitrary sequence and only O(1) amortized time per access during a sequential access sequence.Furthermore, we generalize the standard framework for Competitive analysis of BST algorithms to include updates (insertions and deletions) in addition to queries. In doing so, we extend the lower bound of Wilber [Wil89] and Demaine et al. [DHIP04] to handle these update operations. We show how MSTs can be modified to support these update operations and be O(log log n)-Competitive in the new framework while maintaining the rest of the properties above.
John M. Pearson - One of the best experts on this subject based on the ideXlab platform.
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Bayesian nonparametric models characterize instantaneous strategies in a Competitive Dynamic game.
Nature communications, 2019Co-Authors: Kelsey Mcdonald, William F. Broderick, Scott A. Huettel, John M. PearsonAbstract:Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve Dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. Here, using a game in which humans competed against both real and artificial opponents, we show that it is possible to quantify the instantaneous Dynamic coupling between agents. Adopting a reinforcement learning approach, we use Gaussian Processes to model the policy and value functions of participants as a function of both game state and opponent identity. We found that higher-scoring participants timed their final change in direction to moments when the opponent’s counter-strategy was weaker, while lower-scoring participants less precisely timed their final moves. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of experimental paradigms for assessing behavior. Game theory typically models strategic human behavior using scenarios with decision constraints that poorly represent real-world social interactions. Here, the authors show it is possible to model Dynamic, real-world strategic interactions using Bayesian and reinforcement learning principles.
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Bayesian Nonparametric Models Characterize Instantaneous Strategies in a Competitive Dynamic Game
2018Co-Authors: Kelsey Mcdonald, William F. Broderick, Scott A. Huettel, John M. PearsonAbstract:Previous approaches to investigating strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. However, most real-world social behaviors involve Dynamic, coevolving decisions by interacting agents, which pose challenges for creating tractable models of behavior. Here, using a Competitive game in which human participants control the Dynamics of an on-screen avatar against either another human or a computer opponent, we show that it is possible to quantify the Dynamic coupling between agents using nonparametric models. We use Gaussian Processes to model the joint distributions of players' actions and identities (human or computer) as a function of game state. Borrowing from a reinforcement learning framework, we successfully approximated both the policy and the value functions used by each human player in this Competitive context. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of tractable paradigms for assessing human behavior.