Modelling context awareness for a situated semantic agent

Piek Vossen; Lenka Bajčetić; Selene Baez; Suzana Bašić; Bram Kraaijeveld

doi:10.1007/978-3-030-34974-5_20

Modelling context awareness for a situated semantic agent

Piek Vossen^*, Lenka Bajčetić, Selene Baez, Suzana Bašić, Bram Kraaijeveld

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.

Original language	English
Title of host publication	Modeling and Using Context
Subtitle of host publication	11th International and Interdisciplinary Conference, CONTEXT 2019, Trento, Italy, November 20–22, 2019, Proceedings
Editors	Gábor Bella, Paolo Bouquet
Publisher	Springer
Pages	238-252
Number of pages	15
ISBN (Electronic)	9783030349745
ISBN (Print)	9783030349738
DOIs	https://doi.org/10.1007/978-3-030-34974-5_20
Publication status	Published - 2019
Event	11th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2019 - Trento, Italy Duration: 20 Nov 2019 → 22 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11939 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2019
Country/Territory	Italy
City	Trento
Period	20/11/19 → 22/11/19

Funding

This project was funded through the NWO-Spinoza funds awarded to Piek Vossen and by the VU University of Amsterdam. We specifically thanks Selene Kolman and Bob van Graft for their support.

Keywords

Discourse context
Robotics
Situated context
Social context

VU Research Profile

Connected World

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10.1007/978-3-030-34974-5_20

Modelling Context Awareness for a Situated Semantic AgentFinal published version, 1.17 MBLicence: Article 25fa Dutch Copyright Act

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Vossen, P., Bajčetić, L., Baez, S., Bašić, S., & Kraaijeveld, B. (2019). Modelling context awareness for a situated semantic agent. In G. Bella, & P. Bouquet (Eds.), Modeling and Using Context: 11th International and Interdisciplinary Conference, CONTEXT 2019, Trento, Italy, November 20–22, 2019, Proceedings (pp. 238-252). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11939 LNAI). Springer. https://doi.org/10.1007/978-3-030-34974-5_20

Vossen, Piek ; Bajčetić, Lenka ; Baez, Selene et al. / Modelling context awareness for a situated semantic agent. Modeling and Using Context: 11th International and Interdisciplinary Conference, CONTEXT 2019, Trento, Italy, November 20–22, 2019, Proceedings. editor / Gábor Bella ; Paolo Bouquet. Springer, 2019. pp. 238-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.",

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author = "Piek Vossen and Lenka Baj{\v c}eti{\'c} and Selene Baez and Suzana Ba{\v s}i{\'c} and Bram Kraaijeveld",

year = "2019",

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Vossen, P, Bajčetić, L, Baez, S, Bašić, S & Kraaijeveld, B 2019, Modelling context awareness for a situated semantic agent. in G Bella & P Bouquet (eds), Modeling and Using Context: 11th International and Interdisciplinary Conference, CONTEXT 2019, Trento, Italy, November 20–22, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11939 LNAI, Springer, pp. 238-252, 11th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2019, Trento, Italy, 20/11/19. https://doi.org/10.1007/978-3-030-34974-5_20

Modelling context awareness for a situated semantic agent. / Vossen, Piek; Bajčetić, Lenka; Baez, Selene et al.
Modeling and Using Context: 11th International and Interdisciplinary Conference, CONTEXT 2019, Trento, Italy, November 20–22, 2019, Proceedings. ed. / Gábor Bella; Paolo Bouquet. Springer, 2019. p. 238-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11939 LNAI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Bašić, Suzana

AU - Kraaijeveld, Bram

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N2 - This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.

AB - This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.

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ER -

Vossen P, Bajčetić L, Baez S, Bašić S, Kraaijeveld B. Modelling context awareness for a situated semantic agent. In Bella G, Bouquet P, editors, Modeling and Using Context: 11th International and Interdisciplinary Conference, CONTEXT 2019, Trento, Italy, November 20–22, 2019, Proceedings. Springer. 2019. p. 238-252. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2019 Nov 10. doi: 10.1007/978-3-030-34974-5_20

Modelling context awareness for a situated semantic agent

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