Abstract
TCP-nets are graphical tools for modeling user’s preference and relative importance statements. We propose the Probabilistic TCP-net (PTCP-net) model that can represent a set of TCP-nets, in a compact form, sharing the same set of variables and their domains but having different preference and relative importance statements. In particular, the PTCP-net is able to express the choices of multiple unknown users such as, recommender systems. The PTCP-net can also be seen as an extension of the TCP-net with uncertainty on preference and relative importance statements. We have adopted the Bayesian Network as the reasoning tool for PTCP-nets especially when answering the following two questions (1) finding the most probable TCP-net for a given PTCP-net and (2) finding the most probable optimal outcome.
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Ahmed, S., Mouhoub, M. (2017). Probabilistic TCP-net. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_34
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DOI: https://doi.org/10.1007/978-3-319-57351-9_34
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