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» A Symbolic Symbolic State Space Representation
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NC
1998
140views Neural Networks» more  NC 1998»
15 years 7 months ago
Recurrent Neural Networks with Iterated Function Systems Dynamics
We suggest a recurrent neural network (RNN) model with a recurrent part corresponding to iterative function systems (IFS) introduced by Barnsley 1] as a fractal image compression ...
Peter Tiño, Georg Dorffner
CCA
2005
Springer
15 years 11 months ago
Representing Probability Measures using Probabilistic Processes
In the Type-2 Theory of Effectivity, one considers representations of topological spaces in which infinite words are used as “names” for the elements they represent. Given s...
Matthias Schröder, Alex K. Simpson
CI
2005
106views more  CI 2005»
15 years 6 months ago
Incremental Learning of Procedural Planning Knowledge in Challenging Environments
Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning ...
Douglas J. Pearson, John E. Laird
NGC
2010
Springer
183views Communications» more  NGC 2010»
15 years 26 days ago
Brain-like Computing Based on Distributed Representations and Neurodynamics
A key to overcoming the limitations of classical artificial intelligence and to deal well with enormous amounts of information might be brain-like computing in which distributed re...
Ken Yamane, Masahiko Morita
140
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JAIR
2006
138views more  JAIR 2006»
15 years 6 months ago
Logical Hidden Markov Models
Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characte...
Kristian Kersting, Luc De Raedt, Tapani Raiko