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FLAIRS
2004
15 years 8 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
ADCM
2008
112views more  ADCM 2008»
15 years 7 months ago
Frames in spaces with finite rate of innovation
Abstract. Signals with finite rate of innovation are those signals having finite degrees of freedom per unit of time that specify them. In this paper, we introduce a prototypical s...
Qiyu Sun
SBBD
2004
106views Database» more  SBBD 2004»
15 years 8 months ago
Twisting the Metric Space to Achieve Better Metric Trees
In order to index text fields in XML databases for similarity queries, M-Trees may be applied. However, some datasets experiments using M-Trees for this purpose resulted in low qu...
César Feijó Nadvorny, Carlos A. Heus...
EUROCRYPT
2004
Springer
16 years 19 days ago
Merkle Tree Traversal in Log Space and Time
Abstract. We present a technique for Merkle tree traversal which requires only logarithmic space and time1 . For a tree with N nodes, our algorithm computes sequential tree leaves ...
Michael Szydlo
AAAI
2012
13 years 9 months ago
Planning in Factored Action Spaces with Symbolic Dynamic Programming
We consider symbolic dynamic programming (SDP) for solving Markov Decision Processes (MDP) with factored state and action spaces, where both states and actions are described by se...
Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tad...