Ontology Enrichment for Multi-Domain Knowledge and Expertise Representation

Achsan, Harry Tursulistyono Yani; Suhartanto, Heru; Wibowo, Wahyu Catur

doi:10.2507/28th.daaam.proceedings.162

Cited by 3 publications

(1 citation statement)

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“…All BAS shop floor elements (assembly stations, mobile robots, shop floor operators) produce digital data which is recorded. One of the advantages of the IAM is its ability to use this recorded data to produce and accumulate new system specific knowledge [14]. This makes it possible for the quality of IAM proposals to constantly improve.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Adviser Module: Proposals and Adaptive Learning Capabilities

Hasković¹,

Katalinic²,

Zec³

et al. 2017

DAAAM Proceedings

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This paper presents the Intelligent Adviser Module (IAM) as a decision support tool for the system operator in Bionic Assembly System (BAS). Here, the system operator is the main human decision maker. To achieve high work efficiency of BAS, he needs to make high quality decisions in limited time and with incomplete information about the actual system state and its components. This time shortage and fragmented information brings stress to the system operator and forces him to make lower quality decisions. In such situations he uses the proposals from the IAM where he can accept, reject or inspect them. These proposals are based on many parameters and also on the digitally recorded data from all shop floor elements. The IAM collects this data and from it produces new system specific knowledge. The accuracy of IAM proposals heavily depends on the validity and usability of such acquired data. A new algorithm is developed and described which enables the IAM to collect, index and verify new data through adaptive learning. As a result, any new shop floor element behaviour can be compensated. This demonstrates the adaptive nature of the IAM as an integral part of the BAS control structure.

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Section: Introductionmentioning

confidence: 99%