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244
RealTime A* Search With Depthk Lookahead
, 2009
"... We consider realtime planning problems in which some states are unsolvable, i.e., have no path to a goal. Such problems are difficult for realtime planning algorithms such as RTA* in which all states must be solvable. We identify a property called ksafeness, in which the consequences of a bad cho ..."
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Cited by 2 (0 self)
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We consider realtime planning problems in which some states are unsolvable, i.e., have no path to a goal. Such problems are difficult for realtime planning algorithms such as RTA* in which all states must be solvable. We identify a property called ksafeness, in which the consequences of a bad
Thinking Ahead in RealTime Search
, 2009
"... We consider realtime planning problems in which some states are unsolvable, i.e., have no path to a goal. Such problems are difficult for realtime planning algorithms such as RTA * in which all states must be solvable. We identify a property called ksafeness, in which the consequences of a bad ch ..."
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We consider realtime planning problems in which some states are unsolvable, i.e., have no path to a goal. Such problems are difficult for realtime planning algorithms such as RTA * in which all states must be solvable. We identify a property called ksafeness, in which the consequences of a bad
Calculating Minimum KUnsafe and Maximum KSafe Sets of Variables for Disclosure Risk Assessment of Individual Records in a Microdata Set
"... In the framework of disclosure control of a microdata set, an unique record is at risk of being identified. Even if a record is not unique in the microdata set, it may be considered risky if the frequency k of the cell, in which the record falls, is small. The notion of minimum unsafe combination in ..."
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introduced by Willenborg and de Waal (1996) is important in this respect. The purpose of this paper is to clearly define closely related notions and give an algorithm for obtaining relevant combinations of variables. We will define minimum kunsafe and maximum ksafe sets of variables for each record
Generalization in Reinforcement Learning: Safely Approximating the Value Function
 Advances in Neural Information Processing Systems 7
, 1995
"... To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a genera ..."
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Cited by 307 (4 self)
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an entirely wrong policy. We then introduce GrowSupport, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization. 1 INTRODUCTION Reinforcement learningthe problem of getting an agent to learn to act from sparse, delayed rewardshas been advanced
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil
kArbiter: A safe and general Scheme for hout ofk mutual exclusion problems
, 1995
"... : Mutual exclusion is a wellknown problem that arise when multiple processes compete, in an uncoordinated way, for the acquisition of shared resources over a distributed system. In particular, kmutual exclusion allows at most k processes to get one unit of the same resource simultaneously. These p ..."
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Cited by 14 (3 self)
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: Mutual exclusion is a wellknown problem that arise when multiple processes compete, in an uncoordinated way, for the acquisition of shared resources over a distributed system. In particular, kmutual exclusion allows at most k processes to get one unit of the same resource simultaneously
Discrete Logarithms in Finite Fields and Their Cryptographic Significance
, 1984
"... Given a primitive element g of a finite field GF(q), the discrete logarithm of a nonzero element u GF(q) is that integer k, 1 k q  1, for which u = g k . The wellknown problem of computing discrete logarithms in finite fields has acquired additional importance in recent years due to its appl ..."
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Cited by 105 (7 self)
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Given a primitive element g of a finite field GF(q), the discrete logarithm of a nonzero element u GF(q) is that integer k, 1 k q  1, for which u = g k . The wellknown problem of computing discrete logarithms in finite fields has acquired additional importance in recent years due to its
SelfStabilizing Algorithms for Maximal 2packing and General kpacking (k ≥ 2) with Safe Convergence in an Arbitrary Graph
, 2015
"... In a graph or a network G = (V,E), a set S ⊆ V is a 2packing if ∀i ∈ V: N [i] ∩ S  ≤ 1, where N [i] denotes the closed neighborhood of node i. A 2packing is maximal if no proper superset of S is a 2packing. This paper presents a safely converging selfstabilizing algorithm for maximal 2packi ..."
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packing problem. Under a synchronous daemon, it quickly converges to a 2packing (a safe state, not necessarily the legitimate state) in three synchronous steps, and then terminates in a maximal one (the legitimate state) in O(n) steps without breaking safety during the convergence interval, where n
K.L.: Consistency techniques for flowbased projectionsafe global cost functions in weighted constraint satisfaction
 JAIR
, 2012
"... Abstract Many combinatorial problems deal with preferences and violations, the goal of which is to find solutions with the minimum cost. Weighted constraint satisfaction is a framework for modeling such problems, which consists of a set of cost functions to measure the degree of violation or prefer ..."
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Cited by 4 (3 self)
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Abstract Many combinatorial problems deal with preferences and violations, the goal of which is to find solutions with the minimum cost. Weighted constraint satisfaction is a framework for modeling such problems, which consists of a set of cost functions to measure the degree of violation
BinomialTree Fault Tolerant Routing in DualCubes with Large Number of Faulty Nodes
"... Abstract. A dualcube DC(m) has m + 1 links per node where m is the degree of a cluster (mcube), and one extra link is used for connection between clusters. The dualcube mitigates the problem of increasing number of links in the largescale hypercube network while keeps most of the topological pro ..."
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Cited by 1 (0 self)
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properties of the hypercube network. In this paper, we propose efficient algorithms for finding a nonfaulty routing path between any two nonfaulty nodes in the dualcube with a large number of faulty nodes. A node v ∈ DC(m) is called ksafe if v has at least k nonfaulty neighbors. The DC(m) is called ksafe
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