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Regularised Loss Function for Goal Recognition as a Deep Learning Task
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AI
DOI：10.1007/978-3-032-04558-4_46.png)
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Abstract
En 中文
Goal Recognition (GR) consists of recognising the goal of an agent from partial observations. The state of the art on particular planning domains is represented by GRNet, a model based on Recurrent Neural Networks that solves GR as a classification task. Compared to automated planning, the need for large training sets is the main disadvantage of these approaches. Therefore, we formalise a loss regularisation technique to reduce the number of training samples needed, to reduce the convergence time, and to increase the performance in GR instances with a small percentage of observations. We empirically evaluate its effectiveness through extensive experiments.
Keywords:
Goal Recognition
Loss Regularisation
Recurrent Neural Networks
Deep Learning
Training Sample Reduction
Convergence Time
Partial Observations
Automated Planning
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