User intent to support proactivity in a pervasive system

Shaaban, YA; McBurney, S; Taylor, N; Williams, MH; Kalatzis, N; Roussaki, I

dc.contributor.author	Shaaban, YA	en
dc.contributor.author	McBurney, S	en
dc.contributor.author	Taylor, N	en
dc.contributor.author	Williams, MH	en
dc.contributor.author	Kalatzis, N	en
dc.contributor.author	Roussaki, I	en
dc.date.accessioned	2014-03-01T02:52:14Z
dc.date.available	2014-03-01T02:52:14Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35875
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84859071144&partnerID=40&md5=5aa7753f95df0e008b0d6bd68a57761a	en
dc.subject.other	Formal definition	en
dc.subject.other	Pervasive systems	en
dc.subject.other	Proactivity	en
dc.subject.other	Task models	en
dc.subject.other	User context	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Forecasting	en
dc.title	User intent to support proactivity in a pervasive system	en
heal.type	conferenceItem	en
heal.publicationDate	2009	en
heal.abstract	In a pervasive system it is essential to understand the intent of the user in order to predict his/her future behaviour. This in turn will help to minimise the user's administrative overheads and assist the user to achieve his/her goals. The aim of this paper is to present some aspects of how user intent may be handled. It focuses on the architecture supporting the proactive features of the Persist pervasive platform. A formal definition of the task discovery problem in user intent is provided. The use of the discovered task model to predict the user's next intended task/action is introduced including the way in which user context can assist in the prediction of the user's intended task/action.	en
heal.journalName	Adaptive and Emergent Behaviour and Complex Systems - Proceedings of the 23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009	en
dc.identifier.spage	3	en
dc.identifier.epage	8	en