Abstract
Goal recognition is the task of identifying the goal an observed agent is pursuing. The quality of its results depends on the quality of the observed information. In most goal recognition approaches, the accuracy significantly decreases in settings with missing observations. To mitigate this issue, we develop a learning model based on LSTMs, leveraging attention mechanisms, to enhance observed traces by predicting missing observations in goal recognition problems. We experiment using a dataset of goal recognition problems and apply the model to enhance the observation traces where missing. We evaluate the technique using a state-of-the-art goal recognizer in four different domains to compare the accuracy between the standard and the enhanced observation traces. Experimental evaluation shows that recurrent neural networks with self-attention mechanisms improve the accuracy metrics of state-of-the-art goal recognition techniques by an average of 60%.
Original language | English |
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Title of host publication | 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-8 |
Number of pages | 8 |
ISBN (Electronic) | 9781728169262 |
DOIs | |
Publication status | Published - 28 Sept 2020 |
Event | 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 |
Publication series
Name | Proceedings of the International Joint Conference on Neural Networks |
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Conference
Conference | 2020 International Joint Conference on Neural Networks, IJCNN 2020 |
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Country/Territory | United Kingdom |
City | Virtual, Glasgow |
Period | 19/07/20 → 24/07/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.