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ECAI
2010
Springer
13 years 6 months ago
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Dominik Jain, Andreas Barthels, Michael Beetz
ISIPTA
2003
IEEE
14 years 2 months ago
Combining Belief Functions Issued from Dependent Sources
Dempsterā€™s rule for combining two belief functions assumes the independence of the sources of information. If this assumption is questionable, I suggest to use the least speciļ¬...
Marco E. G. V. Cattaneo
CALCO
2009
Springer
150views Mathematics» more  CALCO 2009»
14 years 3 months ago
Approximating Labelled Markov Processes Again!
Abstract. Labelled Markov processes are continuous-state fully probabilistic labelled transition systems. They can be seen as co-algebras of a suitable monad on the category of mea...
Philippe Chaput, Vincent Danos, Prakash Panangaden...
DATE
2006
IEEE
151views Hardware» more  DATE 2006»
14 years 3 months ago
Designing MRF based error correcting circuits for memory elements
As devices are scaled to the nanoscale regime, it is clear that future nanodevices will be plagued by higher soft error rates and reduced noise margins. Traditional implementation...
Kundan Nepal, R. Iris Bahar, Joseph L. Mundy, Will...
ATAL
2006
Springer
14 years 19 days ago
Rule value reinforcement learning for cognitive agents
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...
Christopher Child, Kostas Stathis