By José Viterbo, Markus Endler
In Ambient Intelligence (AmI) structures, reasoning is key for triggering activities or variations based on particular occasions which may be significant and appropriate to a couple purposes. notwithstanding, such reasoning operations might have to judge context information gathered from dispensed resources and kept in numerous units, as often no longer all context facts is instantly to be had to the reasoners in the process.
Decentralized Reasoning in Ambient Intelligence proposes a decentralized reasoning method for acting rule-based reasoning approximately context information focusing on AmI structures. For this function, the authors outline a context version assuming context facts dispensed over facets: the consumer part, represented by means of the clients and their cellular units, and the ambient facet, represented through the fastened computational infrastructure and ambient companies. They formalize the cooperative reasoning operation — during which entities cooperate to accomplish decentralized rule-based reasoning — and outline an entire method to accomplish this operation.
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As an option, the device at the user side could collect and store all context information available at the ambient side that would be necessary for reasoning about the proposed rules. To follow this approach, however, would entail another problem: the reasoning computation may be too heavy to be performed by the resource-limited mobile device. In fact, we can only expect that inference operations be efficiently executed on a mobile device when the corresponding rules refer exclusively to context information available at the device, rather than provided by the ambient.
F. Sect. 1), at the user side Silva’s notebook stores information about his affiliation, subjects of interest, sessions, besides his devices’ resources and location. , projectors that are turned on), volume of sound in the rooms, etc. 1 shows an example of some context information that might be available on each side at a given moment. Both sides share a common context model, described Sect. 4, but have different context information, as will be formalized in Sect. 4. , the ontology facts, are available either on the user side, such as “wantsToAttend”, or on the user side, such as “takesPlace”, but never on both sides.
The Ontology Server also provides an interface for adding new concepts to existing ontologies. Reasoning Approach Context Synthesizers are Gaia components that collect context data from various Context Providers, derive higher level or abstract context from these lower-level context data and provide these inferred contexts to applications. Whenever a Context Synthesizer deduces a change in the inferred context, it publishes the new information. Gaia adopts two basic inference approaches . Rule-based Synthesizers use predefined rules written in first order logic to infer different contexts.
Decentralized Reasoning in Ambient Intelligence by José Viterbo, Markus Endler