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» State Space Compression with Predictive Representations
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ECAI
2008
Springer
13 years 9 months ago
Reinforcement Learning with the Use of Costly Features
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Robby Goetschalckx, Scott Sanner, Kurt Driessens
ANCS
2006
ACM
14 years 1 months ago
Advanced algorithms for fast and scalable deep packet inspection
Modern deep packet inspection systems use regular expressions to define various patterns of interest in network data streams. Deterministic Finite Automata (DFA) are commonly used...
Sailesh Kumar, Jonathan S. Turner, John Williams
ATAL
2007
Springer
13 years 11 months ago
Interactive dynamic influence diagrams
This paper extends the framework of dynamic influence diagrams (DIDs) to the multi-agent setting. DIDs are computational representations of the Partially Observable Markov Decisio...
Kyle Polich, Piotr J. Gmytrasiewicz
CORR
2010
Springer
102views Education» more  CORR 2010»
13 years 7 months ago
Stable Takens' Embeddings for Linear Dynamical Systems
Takens' Embedding Theorem remarkably established that concatenating M previous outputs of a dynamical system into a vector (called a delay coordinate map) can be a one-to-one...
Han Lun Yap, Christopher J. Rozell
PVLDB
2008
118views more  PVLDB 2008»
13 years 7 months ago
Efficient top-k processing over query-dependent functions
We study the efficient evaluation of top-k queries over data items, where the score of each item is dynamically computed by applying an item-specific function whose parameter valu...
Lin Guo, Sihem Amer-Yahia, Raghu Ramakrishnan, Jay...