Modeling interleaved hidden processes
Proceedings of the 25th international conference on Machine learning
Logical Hierarchical Hidden Markov Models for Modeling User Activities
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Simple Model for Sequences of Relational State Descriptions
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks by symbolic probability calculation based on zero-suppressed BDDs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A simple-transition model for relational sequences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
Recognizing activities with multiple cues
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Probabilistic inductive logic programming
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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One of the current challenges in artificial intelligence is modeling dynamic environments that change due to the actions or activities undertaken by people or agents. The task of inferring hidden states, e.g. the activities or intentions of people, based on observations is called filtering. Standard probabilistic models such as Dynamic Bayesian Networks are able to solve this task efficiently using approximative methods such as particle filters. However, these models do not support logical or relational representations. The key contribution of this paper is the upgrade of a particle filter algorithm for use with a probabilistic logical representation through the definition of a proposal distribution. The performance of the algorithm depends largely on how well this distribution fits the target distribution. We adopt the idea of logical compilation into Binary Decision Diagrams for sampling. This allows us to use the optimal proposal distribution which is normally prohibitively slow.