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Vector space architecture for emergent interoperability of systems by learning from demonstration
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-4133-3317
2015 (English)In: Biologically Inspired Cognitive Architectures, ISSN 2212-683X, E-ISSN 2212-6848, Vol. 11, p. 53-64Article in journal (Refereed) Published
Abstract [en]

The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration.

Place, publisher, year, edition, pages
2015. Vol. 11, p. 53-64
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
URN: urn:nbn:se:ltu:diva-15157DOI: 10.1016/j.bica.2014.11.015ISI: 000352744600006Scopus ID: 2-s2.0-84923018996Local ID: ea324b4f-5f65-478b-bad8-4cb70e37a824OAI: oai:DiVA.org:ltu-15157DiVA, id: diva2:988130
Note
Validerad; 2015; Nivå 2; 20150226 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Emruli, BlerimSandin, FredrikDelsing, Jerker

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