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» Structure learning of Bayesian networks using constraints
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JAIR
2008
138views more  JAIR 2008»
14 years 12 months ago
Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes
This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents' beliefs and decision-making processes....
Ya'akov Gal, Avi Pfeffer
AMAI
1999
Springer
14 years 11 months ago
Maintenance scheduling problems as benchmarks for constraint algorithms
The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the el...
Daniel Frost, Rina Dechter
JMLR
2010
144views more  JMLR 2010»
14 years 6 months ago
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables
In this paper we address the problem of predicting when the available data is incomplete. We show that changing the generally accepted table-wise view of the sample items into a g...
Sándor Szedmák, Yizhao Ni, Steve R. ...
ICML
2009
IEEE
16 years 21 days ago
Proto-predictive representation of states with simple recurrent temporal-difference networks
We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...
Takaki Makino
CMOT
1999
143views more  CMOT 1999»
14 years 11 months ago
Structural Learning: Attraction and Conformity in Task-Oriented Groups
This study extends previous research that showed how informal social sanctions can backfire when members prefer friendship over enforcement of group norms. We use a type of neural...
James A. Kitts, Michael W. Macy, Andreas Flache