Fully generated scripted dialogue for embodied agents

Deemter, Kees van; Krenn, Brigitte; Piwek, Paul; Klesen, Martin; Schröder, Marc; Baumann, Stefan

doi:10.1016/j.artint.2008.02.002

Cited by 35 publications

(19 citation statements)

References 50 publications

(44 reference statements)

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“…Probably the earliest research on context focused on linguistic phenomena, studying how the understanding of an expression (e.g., a personal pronoun like "it") is affected by the rest of the sentence or text [5]. Later research applied these ideas to other aspects of communication, including speech (e.g., pitch accent) and body language (e.g., [12]). Even more drastically, the notion of a context has been extended to all symbolic systems (e.g., [13]).…”

Section: A Context In Cognitive Science and Roboticsmentioning

confidence: 99%

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

Çelikkanat

Orhan

Pugeault

et al. 2016

IEEE Trans. Cogn. Dev. Syst.

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Abstract-In this article, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field, inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make it incremental so that (i) it does not re-learn everything from scratch given new interactions (i.e., it is online) and (ii) it can discover and add a new context into its model when necessary. We demonstrate on the iCub platform that, partly owing to modeling context on top of the concept web, our approach is adaptive, online and robust: It is adaptive and online since it can learn and discover a new context from new interactions. It is robust since it is not affected by irrelevant stimuli and it can discover contexts after a few interactions only. Moreover, we show how to use the context learned in such a model for two important tasks: object recognition and planning.

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Section: A Context In Cognitive Science and Roboticsmentioning

confidence: 99%

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

Çelikkanat

Orhan

Pugeault

et al. 2016

IEEE Trans. Cogn. Dev. Syst.

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“…With FGSD entire dialogues are produced by one generator. Initially, scripted dialogues made heavy use of canned text, but recently this approach has been integrated with Natural Language Generation techniques (Van Deemter et al, 2008;Piwek, 2008). FGSD allows us to produce dialogues, without implementing a full natural language interpretation module.…”

Section: Settingmentioning

confidence: 99%

Cross-cultural assessment of automatically generated multimodal referring expressions in a virtual world

Sluis

Luz

Breitfuí

et al. 2012

International Journal of Human-Computer Studies

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“…pitch accent) and body language (e.g. [11]). Even more drastically, the notion of a context has been extended to all symbolic systems (e.g., [12]).…”

Section: A Context In Cognitive Science and Roboticsmentioning

confidence: 99%

Learning and using context on a humanoid robot using latent dirichlet allocation

Çelikkanat

Orhan

Pugeault

et al. 2014

4th International Conference on Development and Learning and on Epigenetic Robotics

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Abstract-In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts. We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.

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Fully generated scripted dialogue for embodied agents

Abstract: Stefan (2008). Fully generated scripted dialogue for embodied agents. Artificial Intelligence, 172(10) pp. 1219-1244. For guidance on citations see FAQs.

Cited by 35 publications

References 50 publications

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

Cross-cultural assessment of automatically generated multimodal referring expressions in a virtual world

Learning and using context on a humanoid robot using latent dirichlet allocation

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