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3,736
Predicting solution costs with conditional probabilities
 In SoCS
, 2011
"... Classical heuristic search algorithms find the solution cost of a problem while finding the path from the start state to a goal state. However, there are applications in which finding the path is not needed. In this paper we propose an algorithm that accurately and efficiently predicts the solution ..."
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Cited by 3 (3 self)
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Classical heuristic search algorithms find the solution cost of a problem while finding the path from the start state to a goal state. However, there are applications in which finding the path is not needed. In this paper we propose an algorithm that accurately and efficiently predicts
Predicting optimal solution cost with bidirectional stratified sampling
 In ICAPS
, 2012
"... Optimal planning and heuristic search systems solve statespace search problems by finding a leastcost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for ..."
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Cited by 5 (5 self)
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to return the optimal solution cost in the limit as the sample size goes to infinity. We show empirically that our method makes accurate predictions in several domains. In addition, we show that our method scales to state spaces much larger than can be solved optimally. In particular, we estimate
Generalized bestfirst search strategies and the optimality of A*
 JOURNAL OF THE ACM
, 1985
"... This paper reports several properties of heuristic bestfirst search strategies whose scoring functions f depend on all the information available from each candidate path, not merely on the current cost g and the estimated completion cost h. It is shown that several known properties of A * retain t ..."
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Cited by 234 (15 self)
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their form (with the minmax offplaying the role of the optimal cost), which helps establish general tests of admissibility and general conditions for node expansion for these strategies. On the basis of this framework the computational optimality of A*, in the sense of never expanding a node that can
The TPR*Tree: An Optimized SpatioTemporal Access Method for Predictive Queries
 In VLDB
, 2003
"... A predictive spatiotemporal query retrieves the set of moving objects that will intersect a query window during a future time interval. Currently, the only access method for processing such queries in practice is the TPRtree. In this paper we first perform an analysis to determine the factor ..."
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Cited by 191 (10 self)
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takes into account the unique features of dynamic objects through a set of improved construction algorithms. In addition, we provide cost models that determine the optimal performance achievable by any datapartition spatiotemporal access method. Using experimental comparison, we illustrate
Optimal prediction with memory
 PHYSICA D 166 (2002) 239–257
, 2002
"... Optimal prediction methods estimate the solution of nonlinear timedependent problems when that solution is too complex to be fully resolved or when data are missing. The initial conditions for the unresolved components of the solution are drawn from a probability distribution, and their effect on a ..."
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Cited by 46 (6 self)
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Optimal prediction methods estimate the solution of nonlinear timedependent problems when that solution is too complex to be fully resolved or when data are missing. The initial conditions for the unresolved components of the solution are drawn from a probability distribution, and their effect
Samplingbased algorithms for optimal motion planning
 International Journal of Robotics Research (IJRR
"... During the last decade, samplingbased path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidlyexploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to t ..."
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Cited by 187 (14 self)
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as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used samplingbased algorithms converges almost surely to a nonoptimal value. The main
Predicting the Solution Quality in Noisy Optimization
 University of Dortmund
, 2004
"... Abstract. Noise is a common problem encountered in realworld optimization. Although it is folklore that evolution strategies perform well in the presence of noise, even their performance is degraded. One effect on which we will focus in this paper is the reaching of a steady state that deviates fro ..."
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from the actual optimal solution. The quality gain is a local progress measure, describing the expected onegeneration change of the fitness of the population. It can be used to derive evolution criteria and steady state conditions which can be utilized as a starting point to determine the final
Learning and Making Decisions When Costs and Probabilities are Both Unknown
 In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining
, 2001
"... In many machine learning domains, misclassication costs are dierent for dierent examples, in the same way that class membership probabilities are exampledependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be ..."
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Cited by 129 (10 self)
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be learned. This paper rst discusses how to make optimal decisions given cost and probability estimates, and then presents decision tree learning methods for obtaining wellcalibrated probability estimates. The paper then explains how to obtain unbiased estimators for exampledependent costs, taking
HEDGING AND PORTFOLIO OPTIMIZATION UNDER TRANSACTION COSTS: A MARTINGALE APPROACH
, 1996
"... We derive a formula for the minimal initial wealth needed to hedge an arbitrary contingent claim in a continuoustime model with proportional transaction costs; the expression obtained can be interpreted as the supremum of expected discounted values of the claim, over all (pairs of) probability meas ..."
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Cited by 117 (1 self)
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We derive a formula for the minimal initial wealth needed to hedge an arbitrary contingent claim in a continuoustime model with proportional transaction costs; the expression obtained can be interpreted as the supremum of expected discounted values of the claim, over all (pairs of) probability
Results 1  10
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3,736