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• W2290243003 abstract "Plan recognition, the problem of inferring the goals or plans of an observed agent, is a key element of situation awareness in human-machine and machine-machine interactions for many applications. Some plan recognition algorithms require knowledge about the potential behaviours of the observed agent in the form of a plan library, together with a decision model about how the observed agent uses the plan library to make decisions. It is however difficult to elicit and specify the decision model a priori. In this paper, we present a recursive neural network model that learns such a decision model automatically. We discuss promising experimental results of the approach with comparisons to selected state-of-the-art plan recognition algorithms on three benchmark domains." @default.
• W2290243003 created "2016-06-24" @default.
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• W2290243003 date "2015-07-25" @default.
• W2290243003 modified "2023-09-23" @default.
• W2290243003 title "Using a recursive neural network to learn an agent's decision model for plan recognition" @default.
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