Vector space architecture for emergent interoperability of systems by learning from demonstration
(2015) In Biologically Inspired Cognitive Architectures 11. p.53-64- Abstract
- 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 automatically interoperable using handcrafted “semantic” descriptions of services and information are difficult to apply. In this paper we outline a... (More)
- 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 automatically 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a7edb434-2dbe-4913-b32d-cfb00ae96d2c
- author
- Emruli, Blerim LU ; Sandin, Fredrik and Delsing, Jerker
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Biologically Inspired Cognitive Architectures
- volume
- 11
- pages
- 53 - 64
- publisher
- Elsevier
- external identifiers
-
- scopus:84923018996
- ISSN
- 2212-6848
- DOI
- 10.1016/j.bica.2014.11.015
- language
- English
- LU publication?
- no
- id
- a7edb434-2dbe-4913-b32d-cfb00ae96d2c
- date added to LUP
- 2025-03-31 21:36:27
- date last changed
- 2025-04-04 15:06:55
@article{a7edb434-2dbe-4913-b32d-cfb00ae96d2c,
abstract = {{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 automatically 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.}},
author = {{Emruli, Blerim and Sandin, Fredrik and Delsing, Jerker}},
issn = {{2212-6848}},
language = {{eng}},
pages = {{53--64}},
publisher = {{Elsevier}},
series = {{Biologically Inspired Cognitive Architectures}},
title = {{Vector space architecture for emergent interoperability of systems by learning from demonstration}},
url = {{http://dx.doi.org/10.1016/j.bica.2014.11.015}},
doi = {{10.1016/j.bica.2014.11.015}},
volume = {{11}},
year = {{2015}},
}