Linking temporal first-order logic with Bayesian networks for the simulation of pervasive computing systems
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| Title: | Linking temporal first-order logic with Bayesian networks for the simulation of pervasive computing systems |
|---|---|
| Authors: | Katsiri, Eleftheria1 ekatsiri@gmail.com, Mycroft, Alan2 am21@cl.cam.ac.uk |
| Source: | Simulation Modelling Practice & Theory. Jan2011, Vol. 19 Issue 1, p161-180. 20p. |
| Subjects: | Bayesian analysis, Computer systems, Computer simulation, Real-time computing, Ubiquitous computing, Sensor networks |
| Abstract: | Abstract: The authors’ previous work discussed a scalable abstract knowledge representation and reasoning scheme for Pervasive Computing Systems, where both low-level and abstract knowledge is maintained in the form of temporal first-order logic (TFOL) predicates. Furthermore, we introduced a novel concept of a generalised event, an abstract event, which we define as a change in the truth value of an abstract TFOL predicate. Abstract events represent real-time knowledge about the system and they are defined with the help of well-formed TFOL expressions whose leaf nodes are concrete, low-level events using our AESL language. In this paper, we propose to simulate pervasive systems by providing estimated knowledge about its entities and situations that involve them. To achieve this goal, we enhance AESL with higher-order function predicates that denote approximate knowledge about the likelihood of a predicate instance having the value True with respect to a time reference. We define a mapping function between a TFOL predicate and a Bayesian network that calculates likelihood estimates for that predicate as well as a confidence level, i.e., a metric of how reliable the likelihood estimation is for that predicate. Higher-order likelihood predicates are implemented by a novel middleware component, the Likelihood Estimation Service (LES). LES implements the above mapping; first, for each abstract predicate, it learns a Bayesian network that corresponds to that predicate from the knowledge stored in the sensor-driven system. Once trained and validated, the Bayesian networks generate a likelihood estimate and a confidence level. This new knowledge is maintained in the middleware as approximate knowledge therefore providing a simulation of the pervasive system, in the absence of real-time data. Last but not least, we describe an experimental evaluation of our system using the Active BAT location system. [ABSTRACT FROM AUTHOR] |
| Copyright of Simulation Modelling Practice & Theory is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 54880756 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Linking temporal first-order logic with Bayesian networks for the simulation of pervasive computing systems – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Katsiri%2C+Eleftheria%22">Katsiri, Eleftheria</searchLink><relatesTo>1</relatesTo><i> ekatsiri@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mycroft%2C+Alan%22">Mycroft, Alan</searchLink><relatesTo>2</relatesTo><i> am21@cl.cam.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Simulation+Modelling+Practice+%26+Theory%22">Simulation Modelling Practice & Theory</searchLink>. Jan2011, Vol. 19 Issue 1, p161-180. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Ubiquitous+computing%22">Ubiquitous computing</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: The authors’ previous work discussed a scalable abstract knowledge representation and reasoning scheme for Pervasive Computing Systems, where both low-level and abstract knowledge is maintained in the form of temporal first-order logic (TFOL) predicates. Furthermore, we introduced a novel concept of a generalised event, an abstract event, which we define as a change in the truth value of an abstract TFOL predicate. Abstract events represent real-time knowledge about the system and they are defined with the help of well-formed TFOL expressions whose leaf nodes are concrete, low-level events using our AESL language. In this paper, we propose to simulate pervasive systems by providing estimated knowledge about its entities and situations that involve them. To achieve this goal, we enhance AESL with higher-order function predicates that denote approximate knowledge about the likelihood of a predicate instance having the value True with respect to a time reference. We define a mapping function between a TFOL predicate and a Bayesian network that calculates likelihood estimates for that predicate as well as a confidence level, i.e., a metric of how reliable the likelihood estimation is for that predicate. Higher-order likelihood predicates are implemented by a novel middleware component, the Likelihood Estimation Service (LES). LES implements the above mapping; first, for each abstract predicate, it learns a Bayesian network that corresponds to that predicate from the knowledge stored in the sensor-driven system. Once trained and validated, the Bayesian networks generate a likelihood estimate and a confidence level. This new knowledge is maintained in the middleware as approximate knowledge therefore providing a simulation of the pervasive system, in the absence of real-time data. Last but not least, we describe an experimental evaluation of our system using the Active BAT location system. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Simulation Modelling Practice & Theory is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.simpat.2010.06.002 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 161 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Computer systems Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Ubiquitous computing Type: general – SubjectFull: Sensor networks Type: general Titles: – TitleFull: Linking temporal first-order logic with Bayesian networks for the simulation of pervasive computing systems Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Katsiri, Eleftheria – PersonEntity: Name: NameFull: Mycroft, Alan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 1569190X Numbering: – Type: volume Value: 19 – Type: issue Value: 1 Titles: – TitleFull: Simulation Modelling Practice & Theory Type: main |
| ResultId | 1 |
