Conversational agents can benefit healthcare across different application domains. However, the automated generation of reliable agents is still challenging and lags behind traditional conversational domains. This research exploited the interplay of information management and automated planning to efficiently model the expected behavior of goal-oriented health dialogues. The proposed approach supports the dynamic generation of predictable policies that are used for the management of the health dialogue as well as the identification of the dialogue state. This work advances the state of the art in health dialogue management by automating the generation (and update) of efficient dialogue managers with a reduced cost since they do not require handcrafting of the dialogue policy or large conversational datasets.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/361402 |
| Date | 16 December 2022 |
| Creators | Santos Teixeira, Milene |
| Contributors | Santos Teixeira, Milene, Dragoni, Mauro, Eccher, Claudio |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:207, numberofpages:207 |
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