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ICML
2005
IEEE
14 years 9 months ago
Learning the structure of Markov logic networks
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Stanley Kok, Pedro Domingos
ICML
2003
IEEE
14 years 9 months ago
Q-Decomposition for Reinforcement Learning Agents
The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and...
Stuart J. Russell, Andrew Zimdars
ICML
2002
IEEE
14 years 9 months ago
Coordinated Reinforcement Learning
We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value fu...
Carlos Guestrin, Michail G. Lagoudakis, Ronald Par...
ICML
1999
IEEE
14 years 9 months ago
Implicit Imitation in Multiagent Reinforcement Learning
Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively obs...
Bob Price, Craig Boutilier
WWW
2008
ACM
14 years 9 months ago
Enhanced hierarchical classification via isotonic smoothing
Hierarchical topic taxonomies have proliferated on the World Wide Web [5, 18], and exploiting the output space decompositions they induce in automated classification systems is an...
Kunal Punera, Joydeep Ghosh