Title :
Approximate Online Inference for Dynamic Markov Logic Networks
Author :
Geier, Thomas ; Biundo, Susanne
Author_Institution :
Inst. of Artificial Intell., Ulm Univ., Ulm, Germany
Abstract :
We examine the problem of filtering for dynamic probabilistic systems using Markov Logic Networks. We propose a method to approximately compute the marginal probabilities for the current state variables that is suitable for online inference. Contrary to existing algorithms, our approach does not work on the level of belief propagation, but can be used with every algorithm suitable for inference in Markov Logic Networks, such as MCSAT. We present an evaluation of its performance on two dynamic domains.
Keywords :
Markov processes; approximation theory; computability; inference mechanisms; probabilistic logic; probability; MCSAT; approximate online inference; belief propagation; dynamic Markov logic networks; dynamic probabilistic systems; marginal probabilities; performance evaluation; Approximation methods; Belief propagation; Computational modeling; Heuristic algorithms; Inference algorithms; Markov processes; Probabilistic logic; dynamic probabilistic inference; markov logic networks; online inference;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2011.120