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• W2891071304 abstract "As our modern-built structures arebecoming increasingly complex, carrying out basic tasks such asidentifying points or objects of interest in our surroundings canconsume considerable time and cognitive resources. In this thesis,we present a computational approach to converting contextualinformation about a person's physical environment into naturallanguage, with the aim of helping this person identify giventask-related entities in their environment. Using efficient methodsfrom automated planning - the field of artificial intelligenceconcerned with finding courses of action that can achieve a goal -,we generate discourse that interactively guides a hearer throughcompleting their task. Our approach addresses the challenges ofcontrolling, adapting to, and monitoring the situated context. Tothis end, we develop a natural language generation system thatplans how to manipulate the non-linguistic context of a scene inorder to make it more favorable for references to task-relatedobjects. This strategy distributes a hearer's cognitive load ofinterpreting a reference over multiple utterances rather than onelong referring expression. Further, to optimize the system'slinguistic choices in a given context, we learn how to distinguishspeaker behavior according to its helpfulness to hearers in acertain situation, and we model the behavior of human speakers thathas been proven helpful. The resulting system combines symbolicwith statistical reasoning, and tackles the problem of makingnon-trivial referential choices in rich context. Finally, wecomplement our approach with a mechanism for preventing potentialmisunderstandings after a reference has been generated. Employingremote eye-tracking technology, we monitor the hearer's gaze andfind that it provides a reliable index of online referentialunderstanding, even in dynamically changing scenes. We thus presenta system that exploits hearer gaze to generate rapid feedback on aper-utterance basis, further enhancing its effectiveness. Though weevaluate our approach in virtual environments, the efficiency ofour planning-based model suggests that this work could be a steptowards effective conversational human-computer interactionsituated in the real world.%%%%Die zunehmende Komplexitat modernerGebaude und Infrastrukturen fuhrt dazu, dass alltaglicheAktivitaten, wie z.B. die Identifizierung von gesuchten Objekten inunserer Umgebung und das Auffinden von Orten, betrachtliche Zeitund kognitive Ressourcen in Anspruch nehmen konnen. In dieserDissertation werden computerbasierte Verfahren prasentiert, welcheeine Person dabei unterstutzen, Zielobjekte in Ihrem Umfeld zuidentifizieren. Dabei werden Informationen uber die Situation unddas physische Umfeld der Person - der sog. situierte Kontext - innaturliche Sprache umgewandelt. So wird Diskurs generiert, dereinen Horer interaktiv zum Erreichen eines Zieles bzw. zumAbschliesen einer Aufgabe fuhrt. Hierbei kommen Methoden aus derPlanung zum Einsatz, einem Gebiet der kunstlichen Intelligenz,welches sich…" @default.
• W2891071304 created "2018-09-27" @default.
• W2891071304 creator A5002834467 @default.
• W2891071304 date "2013-01-01" @default.
• W2891071304 modified "2023-09-23" @default.
• W2891071304 title "Interactivegeneration of effective discourse in situated context : aplanning-based approach" @default.
• W2891071304 hasPublicationYear "2013" @default.
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