Meta Programming with Answer Sets for Smart Spaces

Janhunen, Tomi; Luukkala, Vesa

doi:10.1007/978-3-642-33203-6_9

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Note that SPARQL is not the only way to define such rules. Another promising approach is answer set programming [25], when SIB is enhanced with artificial reasoners for the content.…”

Section: Session Management Contains Operations Join and Leavementioning

confidence: 99%

Design of semantic information broker for localized computing environments in the internet of things

Galov

Lomov

Korzun

2015

2015 17th Conference of Open Innovations Association (FRUCT)

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Emerging communication technologies of the Internet of Things (IoT) make all the devices of a spatial-limited physical computing environment locally interconnected as well as connected to the Internet. Software agents running on devices make the latter "smart objects" that are visible in our daily lives as real participating entities. Based on the M3 architecture for smart spaces, we consider the problem of creating a smart space deploying a Semantic Information Broker (SIB) in a localized IoTenvironment. SIB supports agent interaction in the smart space via sharing and self-generating information and its semantics. This paper proposes a renewed SIB design with increased extensibility, dependability, and portability. The research done is a step towards an efficient open interoperability platform for the smart space application development.

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“…Note that SPARQL is not the only way to define such rules. Another promising approach is answer set programming [25], when SIB is enhanced with artificial reasoners for the content.…”

Section: Session Management Contains Operations Join and Leavementioning

confidence: 99%

Design of semantic information broker for localized computing environments in the internet of things

Galov

Lomov

Korzun

2015

2015 17th Conference of Open Innovations Association (FRUCT)

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“…For example, Gebser et al [19] present a meta-approach to declaratively debug ASP programs. In [22], a meta-level ASP representation is used to manage interacting embedded computational objects that publish their properties as ASP programs.…”

Section: §1 Introductionmentioning

confidence: 99%

“…However, from a practical point of view, we prefer the second solution, where only relevant nodes in the parse tree are taken into account. Conceptually, the latter solution is similar to the approach taken in many applications of metareasoning in the context of ASP [18,19,22]. The biggest difference is that in ASP, one typically enforces restrictions on programs to handle infinite domains: programs are assumed to be safe, i.e., written by a careful programmer to ensure that the grounding is finite, while we allow any FO(•) IDP theory.…”

Section: §1 Introductionmentioning

confidence: 99%

Bootstrapping Inference in the IDP Knowledge Base System

et al. 2016

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Declarative systems aim at solving tasks by running inference engines on a specification, to free its users from having to specify how a task should be tackled. In order to provide such functionality, declarative systems themselves apply complex reasoning techniques, and, as a consequence, the development of such systems can be laborious work. In this paper, we demonstrate that the declarative approach can be applied to develop such systems, by tackling the tasks solved inside a declarative system declaratively. In order to do this, a meta-level representation of those specifications is often required. Furthermore, by using the language of the system for the meta-level representation, it opens the door to bootstrapping: an inference engine can be improved using the inference it performs itself.One such declarative system is the IDP knowledge base system, based on the language FO(•) IDP , a rich extension of first-order logic. In this paper, we discuss how FO(•) IDP can support meta-level representations in general and which language constructs make those representations even more natural. Afterwards, we show how meta-FO(•) IDP can be applied to bootstrap its model expansion inference engine. We discuss the advantages of this approach: the resulting program is easier to understand, easier to maintain, and more flexible.

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