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JMLR
2006
113views more  JMLR 2006»
14 years 9 months ago
Learning the Structure of Linear Latent Variable Models
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
JMLR
2010
128views more  JMLR 2010»
14 years 4 months ago
Learning Causal Structure from Overlapping Variable Sets
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges correspo...
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G...
ROBOCUP
2005
Springer
109views Robotics» more  ROBOCUP 2005»
15 years 3 months ago
Using the Max-Plus Algorithm for Multiagent Decision Making in Coordination Graphs
Abstract. Coordination graphs offer a tractable framework for cooperative multiagent decision making by decomposing the global payoff function into a sum of local terms. Each age...
Jelle R. Kok, Nikos A. Vlassis
AIPS
2007
14 years 12 months ago
On the Hardness of Planning Problems with Simple Causal Graphs
We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial time algorithm that uses macros to generate pla...
Omer Giménez, Anders Jonsson
JAIR
2008
99views more  JAIR 2008»
14 years 9 months ago
The Complexity of Planning Problems With Simple Causal Graphs
We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial-time algorithm that uses macros to generate pla...
Omer Giménez, Anders Jonsson