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Selfimproving algorithms
 in SODA ’06: Proceedings of the seventeenth annual ACMSIAM symposium on Discrete algorithm
"... We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such selfimproving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i) an al ..."
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Cited by 33 (6 self)
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We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such selfimproving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i
Selfimproving Algorithms for Convex Hulls
, 2009
"... We give an algorithm for computing planar convex hulls in the selfimproving model: given a sequence I1, I2,... of planar npoint sets, the upper convex hull conv(I) of each set I is desired. We assume that there exists a probability distribution D on npoint sets, such that the inputs Ij are drawn ..."
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Cited by 2 (2 self)
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We give an algorithm for computing planar convex hulls in the selfimproving model: given a sequence I1, I2,... of planar npoint sets, the upper convex hull conv(I) of each set I is desired. We assume that there exists a probability distribution D on npoint sets, such that the inputs Ij are drawn
Selfimproving Algorithms for Coordinatewise Maxima [Extended Abstract]
"... Computing the coordinatewise maxima of a planar point set is a classic and wellstudied problem in computational geometry. We give an algorithm for this problem in the selfimproving setting. We have n (unknown) independent distributions D1, D2,..., Dn of planar points. An input pointset (p1, p2,... ..."
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Cited by 1 (0 self)
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,..., pn) is generated by taking an independent sample pi from each Di, so the input distribution D is the product i Di. A selfimproving algorithm repeatedly gets input sets from the distribution D (which is a priori unknown) and tries to optimize its running time for D. Our algorithm uses the first few
CS369N: Beyond WorstCase Analysis Lecture #5: SelfImproving Algorithms ∗
, 2010
"... Last lecture concluded with a discussion of semirandom graph models, an interpolation between worstcase analysis and averagecase analysis designed to identify robust algorithms in the face of strong impossibility results for worstcase guarantees. This lecture and the next two give three more ana ..."
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analysis frameworks that blend aspects of worst and averagecase analysis. Today’s model, of selfimproving algorithms, is the closest to traditional averagecase analysis. The model and results are by Ailon, Chazelle, Comandar, and Liu [1]. The Setup. For a given computational problem, we posit a
Selfimproving reactive agents based on reinforcement learning, planning and teaching
 Machine Learning
, 1992
"... Abstract. To date, reinforcement learning has mostly been studied solving simple learning tasks. Reinforcement learning methods that have been studied so far typically converge slowly. The purpose of this work is thus twofold: 1) to investigate the utility of reinforcement learning in solving much ..."
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Cited by 315 (3 self)
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was used as a testbed. The enviromaaent is moderately complex and nondeterministic. This paper describes these frameworks and algorithms in detail and presents empirical evaluation of the frameworks.
Bullet: High Bandwidth Data Dissemination Using an Overlay Mesh
, 2003
"... In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes selforganize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burd ..."
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Cited by 424 (22 self)
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deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to selforganize into a high bandwidth overlay mesh. We construct Bullet around the insight that data
SelfImprovement for the ADATE Automatic Programming System
 In WSES International Conference on Evolutionary Computation
, 2002
"... Automatic Design of Algorithms Through Evolution (ADATE) is a system for automatic programming based on the neutral theory of evolution. This work examines methods of selfimprovement for the ADATE system. ..."
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Automatic Design of Algorithms Through Evolution (ADATE) is a system for automatic programming based on the neutral theory of evolution. This work examines methods of selfimprovement for the ADATE system.
Shifting Inductive Bias with SuccessStory Algorithm, Adaptive Levin Search, and Incremental SelfImprovement
 MACHINE LEARNING
, 1997
"... We study task sequences that allow for speeding up the learner's average reward intake through appropriate shifts of inductive bias (changes of the learner's policy). To evaluate longterm effects of bias shifts setting the stage for later bias shifts we use the "successstory algori ..."
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Cited by 72 (32 self)
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modification strategy within the policy itself (incremental selfimprovement). Our inductive transfer case studies...
Incremental SelfImprovement For LifeTime MultiAgent Reinforcement Learning
 From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior
, 1996
"... Previous approaches to multiagent reinforcement learning are either very limited or heuristic by nature. The main reason is: each agent's or "animat's" environment continually changes because the other learning animats keep changing. Traditional reinforcement learning algorithms ..."
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Cited by 10 (3 self)
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with limited computational resources in an unrestricted, changing environment. The method is called "incremental selfimprovement" (IS  Schmidhuber, 1994). IS properly takes into account that whatever some animat learns at some point may affect learning conditions for other animats or for itself
Beyond "Genetic Programming": Incremental SelfImprovement
 Proc. Workshop on Genetic Programming at ML95
, 1995
"... Back in 1986, Dickmanns, Winklhofer, and the author used a genetic algorithm to evolve variablelength computer programs [4]. Today, our approach would be classified as "Genetic Programming" (GP). We applied it to simple tasks, including the "lawnmower problem" (later also studie ..."
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Back in 1986, Dickmanns, Winklhofer, and the author used a genetic algorithm to evolve variablelength computer programs [4]. Today, our approach would be classified as "Genetic Programming" (GP). We applied it to simple tasks, including the "lawnmower problem" (later also
Results 1  10
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1,953