Constructing flexible dynamic belief networks from firstorder probabilistic knowledge bases (1995)by S. Glesner and D. Koller
Abstract:
This paper investigates the power of firstorder probabilistic logic (FOPL) as a representation language for complex dynamic situations. We introduce a sublanguage of FOPL and use it to provide a firstorder version of dynamic belief networks. We show that this language is expressive enough to enable reasoning over time and to allow procedural representations of conditional probability tables. In particular, we define decision tree representations of conditional probability tables that can be used to decrease the size of the created belief networks. We provide an inference algorithm for our sublanguage using the paradigm of knowledgebased model construction. Given a FOPL knowledge base and a particular situation, our algorithm constructs a propositional dynamic belief network, which can be solved using standard belief network inference algorithms. In contrast to common dynamic belief networks, the structure of our networks is more flexible and better adapted to the given situation. We demonstrate the expressive power of our language and the flexibility of the resulting belief networks using a simple knowledge base modeling the propagation of infectious diseases.
Download Information
S. Glesner and D. Koller (1995). "Constructing flexible dynamic belief networks from firstorder probabilistic knowledge bases." Proceedings of the European Conference on Symbolic andQuantitative Approaches to Reasoning and Uncertainty (ECSQARU '95) (pp. 217226).


Bibtex citation
@inproceedings{Glesner+Koller:95,
author = "S. Glesner and D. Koller",
editor = "Ch. Froidevaux and J. Kohlas",
booktitle = "Proceedings of the European Conference on Symbolic and
Quantitative Approaches to Reasoning and Uncertainty
(ECSQARU '95)",
title = "Constructing flexible dynamic belief networks from
firstorder probabilistic knowledge bases",
publisher = "Springer Verlag",
pages = "217226",
year = "1995",
}
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