Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar

Ardavan Salehi Nobandegani McGill University, Ioannis Psaromiligkos McGill University
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:880-889, 2015.

Abstract

Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. That is, although precise answers to some queries, in principle, cannot be achieved, a range of plausible answers is attainable for each query given the available partial knowledge. In this paper, we propose the Multi-Context Model (MCM), a new graphical model to represent the state of partial knowledge as to a domain. MCM is a middle ground between Probabilistic Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to form partial beliefs regarding a domain in a gradual and probabilistically consistent way, and (ii) how to perform inference, i.e., to evaluate a probability of interest involving some variables of the domain.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-university15u, title = {Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar}, author = {University, Ardavan Salehi Nobandegani McGill and University, Ioannis Psaromiligkos McGill}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {880--889}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/university15u/university15u.pdf}, url = {https://proceedings.mlr.press/r13/university15u.html}, abstract = {Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. That is, although precise answers to some queries, in principle, cannot be achieved, a range of plausible answers is attainable for each query given the available partial knowledge. In this paper, we propose the Multi-Context Model (MCM), a new graphical model to represent the state of partial knowledge as to a domain. MCM is a middle ground between Probabilistic Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to form partial beliefs regarding a domain in a gradual and probabilistically consistent way, and (ii) how to perform inference, i.e., to evaluate a probability of interest involving some variables of the domain.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar %A Ardavan Salehi Nobandegani McGill University %A Ioannis Psaromiligkos McGill University %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-university15u %I PMLR %P 880--889 %U https://proceedings.mlr.press/r13/university15u.html %V R13 %X Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. That is, although precise answers to some queries, in principle, cannot be achieved, a range of plausible answers is attainable for each query given the available partial knowledge. In this paper, we propose the Multi-Context Model (MCM), a new graphical model to represent the state of partial knowledge as to a domain. MCM is a middle ground between Probabilistic Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to form partial beliefs regarding a domain in a gradual and probabilistically consistent way, and (ii) how to perform inference, i.e., to evaluate a probability of interest involving some variables of the domain. %Z Reissued by PMLR on 04 October 2026.
APA
University, A.S.N.M. & University, I.P.M.. (2015). Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:880-889 Available from https://proceedings.mlr.press/r13/university15u.html. Reissued by PMLR on 04 October 2026.

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