37029
2011
2011
eng
239
272
34
2
82
article
Springer
Dordrecht
1
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Stochastic relational processes efficient inference and applications
One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.
Machine learning
10.1007/s10994-010-5213-8
0885-6125 (print)
wos:2011-2013
International Conference on Machine Learning/ Workshop on Machine Learning and Graphs
2008
WOS:000288121900007
Helsinki, FINLAND
Thon, I (reprint author), Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A, B-3001 Heverlee, Belgium., ingo.thon@cs.kuleuven.be; landwehr@cs.uni-potsdam.de; luc.deraedt@cs.kuleuven.be
Ingo Thon
Niels Landwehr
Luc De Raedt
eng
uncontrolled
Statistical relational learning
eng
uncontrolled
Stochastic relational process
eng
uncontrolled
Markov processes
eng
uncontrolled
Time series
eng
uncontrolled
CP-Logic
Institut für Informatik und Computational Science
Referiert
Institut für Informatik