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43
A Recursive Random Search Algorithm for LargeScale Network Parameter Configuration
"... Parameter configuration is a common procedure used in largescale network protocols to support multiple operational goals. This problem can be formulated as a blackbox optimization problem and solved with an efficient search algorithm. This paper proposes a new heuristic search algorithm, Recursi ..."
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Cited by 32 (7 self)
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Parameter configuration is a common procedure used in largescale network protocols to support multiple operational goals. This problem can be formulated as a blackbox optimization problem and solved with an efficient search algorithm. This paper proposes a new heuristic search algorithm, Recursive Random Search(RRS), for largescale network parameter optimization. The RRS algorithm is based on the initial highefficiency property of random sampling and attempts to maintain this highefficiency by constantly "restarting" random sampling with adjusted sample spaces. Due to its root in random sampling, the RRS algorithm is robust to the effect of random noises in the objective function and is advantageous in optimizing the objective function with negligible parameters. These features are
Automated Discovery of Composite SAT VariableSelection Heuristics
, 2002
"... Variants of GSAT and Walksat are among the most successful SAT local search algorithms. We show that several wellknown SAT local search algorithms are the results of novel combinations of a set of variable selection primitives. We describe CLASS, an automated heuristic discovery system which genera ..."
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Cited by 27 (1 self)
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Variants of GSAT and Walksat are among the most successful SAT local search algorithms. We show that several wellknown SAT local search algorithms are the results of novel combinations of a set of variable selection primitives. We describe CLASS, an automated heuristic discovery system which generates new, effective variable selection heuristic functions using a simple composition operator. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty and RNovelty . We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics recently proposed by Schuurmans and Southey.
A smart hillclimbing algorithm for application server configuration
 13th Int. Conf. on WWW
, 2004
"... The overwhelming success of the Web as a mechanism for facilitating information retrieval and for conducting business transactions has led to an increase in the deployment of complex enterprise applications. These applications typically run on Web Application Servers, which assume the burden of mana ..."
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Cited by 26 (0 self)
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The overwhelming success of the Web as a mechanism for facilitating information retrieval and for conducting business transactions has led to an increase in the deployment of complex enterprise applications. These applications typically run on Web Application Servers, which assume the burden of managing many tasks, such as concurrency, memory management, database access, etc., required by these applications. The performance of an Application Server depends heavily on appropriate configuration. Configuration is a difficult and errorprone task due to the large number of configuration parameters and complex interactions between them. We formulate the problem of finding an optimal configuration for a given application as a blackbox optimization problem. We propose a Smart HillClimbing algorithm using ideas of importance sampling and Latin Hypercube Sampling (LHS). The algorithm is efficient in both searching and random sampling. It consists of estimating a local function, and then, hillclimbing in the steepest descent direction. The algorithm also learns from past searches and restarts in a smart and selective fashion using the idea of importance sampling. We have carried out extensive experiments with an online brokerage application running in a WebSphere environment. Empirical results demonstrate that our algorithm is more efficient than and superior to traditional heuristic methods. Categories and Subject Descriptors
A unifying framework for computational reinforcement learning theory
, 2009
"... Computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervisedlearning algorithms such as their sample complexity. While existing models such as PAC (Probably Approximately Correct) have played an influential role in understand ..."
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Cited by 18 (6 self)
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Computational learning theory studies mathematical models that allow one to formally analyze and compare the performance of supervisedlearning algorithms such as their sample complexity. While existing models such as PAC (Probably Approximately Correct) have played an influential role in understanding the nature of supervised learning, they have not been as successful in reinforcement learning (RL). Here, the fundamental barrier is the need for active exploration in sequential decision problems. An RL agent tries to maximize longterm utility by exploiting its knowledge about the problem, but this knowledge has to be acquired by the agent itself through exploring the problem that may reduce shortterm utility. The need for active exploration is common in many problems in daily life, engineering, and sciences. For example, a Backgammon program strives to take good moves to maximize the probability of winning a game, but sometimes it may try novel and possibly harmful moves to discover how the opponent reacts in the hope of discovering a better gameplaying strategy. It has been known since the early days of RL that a good tradeoff between exploration and exploitation is critical for the agent to learn fast (i.e., to reach nearoptimal strategies
Learning heuristic functions from relaxed plans
 In International Conference on Automated Planning and Scheduling (ICAPS
, 2006
"... We present a novel approach to learning heuristic functions for AI planning domains. Given a state, we view a relaxed plan (RP) found from that state as a relational database, which includes the current state and goal facts, the actions in the RP, and the actions ’ add and delete lists. We represent ..."
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Cited by 15 (5 self)
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We present a novel approach to learning heuristic functions for AI planning domains. Given a state, we view a relaxed plan (RP) found from that state as a relational database, which includes the current state and goal facts, the actions in the RP, and the actions ’ add and delete lists. We represent heuristic functions as linear combinations of generic features of the database, selecting features and weights using training data from solved problems in the target planning domain. Many recent competitive planners use RPbased heuristics, but focus exclusively on the length of the RP, ignoring other RP features. Since RP construction ignores delete lists, for many domains, RP length dramatically underestimates the distance to a goal, providing poor guidance. By using features that depend on deleted facts and other RP properties, our learned heuristics can potentially capture patterns that describe where such underestimation occurs. Experiments in the STRIPS domains of IPC 3 and 4 show that bestfirst search using the learned heuristic can outperform FF (Hoffmann & Nebel 2001), which provided our training data, and frequently outperforms the top performances in IPC 4.
Automated Configuration of Algorithms for Solving Hard Computational Problems
, 2009
"... The bestperforming algorithms for many hard problems are highly parameterized. Selecting the best heuristics and tuning their parameters for optimal overall performance is often a difficult, tedious, and unsatisfying task. This thesis studies the automation of this important part of algorithm desig ..."
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Cited by 15 (8 self)
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The bestperforming algorithms for many hard problems are highly parameterized. Selecting the best heuristics and tuning their parameters for optimal overall performance is often a difficult, tedious, and unsatisfying task. This thesis studies the automation of this important part of algorithm design: the configuration of discrete algorithm components and their continuous parameters to construct an algorithm with desirable empirical performance characteristics. Automated configuration procedures can facilitate algorithm development and be applied on the end user side to optimize performance for new instance types and optimization objectives. The use of such procedures separates highlevel cognitive tasks carried out by humans from tedious lowlevel tasks that can be left to machines. We introduce two alternative algorithm configuration frameworks: iterated local search in parameter configuration space and sequential optimization based on response surface models. To the best of our knowledge, our local search approach is the first that goes beyond local optima. Our modelbased search techniques significantly outperform existing techniques and extend them in ways crucial for general algorithm configuration: they can handle categorical parameters, optimization objectives defined across multiple instances, and tens of thousands
AmbiguityDirected Sampling for Qualitative Analysis of Sparse Data from SpatiallyDistributed Physical Systems
 In Proc. IJCAI
, 2001
"... A number of important scientific and engineering ..."
A System for Building Intelligent Agents that Learn to Retrieve and Extract Information
, 2001
"... We present a system for rapidly and easily building instructable and selfadaptive software agents that retrieve and extract information. Our Wisconsin Adaptive Web Assistant (Wawa) constructs intelligent agents by accepting user preferences in the form of instructions. These userprovided instructi ..."
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Cited by 14 (5 self)
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We present a system for rapidly and easily building instructable and selfadaptive software agents that retrieve and extract information. Our Wisconsin Adaptive Web Assistant (Wawa) constructs intelligent agents by accepting user preferences in the form of instructions. These userprovided instructions are compiled into neural networks that are responsible for the adaptive capabilities of an intelligent agent. The agent's neural networks are modified via userprovided and systemconstructed training examples. Users can create training examples by rating Web pages (or documents) , but more importantly Wawa's agents uses techniques from reinforcement learning to internally create their own examples. Users can also provide additional instruction throughout the life of an agent. Our experimental evaluations on a "homepage finder" agent and a "seminarannouncement extractor" agent illustrate the value of using instructable and adaptive agents for retrieving and extracting information.
Local search with very largescale neighborhoods for optimal permutations in machine translation
 In Proc. of the Workshop on Computationally Hard Problems and Joint Inference
, 2006
"... We introduce a novel decoding procedure for statistical machine translation and other ordering tasks based on a family of Very LargeScale Neighborhoods, some of which have previously been applied to other NPhard permutation problems. We significantly generalize these problems by simultaneously con ..."
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Cited by 12 (3 self)
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We introduce a novel decoding procedure for statistical machine translation and other ordering tasks based on a family of Very LargeScale Neighborhoods, some of which have previously been applied to other NPhard permutation problems. We significantly generalize these problems by simultaneously considering three distinct sets of ordering costs. We discuss how these costs might apply to MT, and some possibilities for training them. We show how to search and sample from exponentially large neighborhoods using efficient dynamic programming algorithms that resemble statistical parsing. We also incorporate techniques from statistical parsing to improve the runtime of our search. Finally, we report results of preliminary experiments indicating that the approach holds promise. 1
Parameter adjustment based on performance prediction: Towards an instanceaware problem solver
 In: Technical Report: MSRTR2005125, Microsoft Research
, 2005
"... Tuning an algorithm’s parameters for robust and high performance is a tedious and timeconsuming task that often requires knowledge about both the domain and the algorithm of interest. Furthermore, the optimal parameter configuration to use may differ considerably across problem instances. In this r ..."
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Cited by 12 (4 self)
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Tuning an algorithm’s parameters for robust and high performance is a tedious and timeconsuming task that often requires knowledge about both the domain and the algorithm of interest. Furthermore, the optimal parameter configuration to use may differ considerably across problem instances. In this report, we define and tackle the algorithm configuration problem, which is to automatically choose the optimal parameter configuration for a given algorithm on a perinstance base. We employ an indirect approach that predicts algorithm runtime for the problem instance at hand and each (continuous) parameter configuration, and then simply chooses the configuration that minimizes the prediction. This approach is based on similar work by LeytonBrown et al. [LBNS02, NLBD + 04] who tackle the algorithm selection problem [Ric76] (given a problem instance, choose the best algorithm to solve it). While all previous studies for runtime prediction focussed on tree search algorithm, we demonstrate that it is possible to fairly accurately predict the runtime of SAPS [HTH02], one of the bestperforming stochastic local search algorithms for SAT. We also show that our approach automatically picks parameter configurations that speed up SAPS by an average factor of more than two when compared to its default parameter configuration. Finally, we introduce sequential Bayesian learning to the problem of runtime prediction, enabling an incremental learning approach and yielding very informative estimates of predictive uncertainty. 1