Abstract
This paper presents the aGrUM framework, a C++ library providing state-of-the-art implementations of graphical models for decision making, including Bayesian Networks, Influence Diagrams, Credal Networks, Probabilistic Relational Models. This is the result of an ongoing effort to build an efficient and well maintained open source cross-platform software, running on Linux, MacOS X and Windows, for dealing with graphical models. The framework also contains a wrapper, pyAgrum, for exploiting aGrUM within Python.
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The website also contains installation instructions, the library’s documentation and support.
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Gonzales, C., Torti, L., Wuillemin, PH. (2017). aGrUM: A Graphical Universal Model Framework. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_20
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DOI: https://doi.org/10.1007/978-3-319-60045-1_20
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