Development of Cognitive Capabilities for Smart Home using a Self-Organizing Fuzzy Neural Network

Ray, Anjan Kumar; Leng, Gang; McGinnity, T.M.; Coleman, Sonya; Maguire, Liam

doi:10.3182/20120905-3-hr-2030.00182

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…deciding what service goal the ecology should enact in any given situation, is the responsibility of the RUBICON Cognitive Layer [46] -a reasoning module built over Self Organising Fuzzy Neural Networks (SOFNNs) [47]. The Cognitive Layer does not analyse sensor data directly, but it reasons over the events already classified by the Learning Layer to learn to predict the need to activate appliances and robotic services.…”

Section: Key Issues In Rubiconmentioning

confidence: 99%

On the Integration of Adaptive and Interactive Robotic Smart Spaces

Dragone

Saunders

Dautenhahn

2015

Paladyn, Journal of Behavioral Robotics

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Date of Acceptance: 14/04/2015. ?? 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user???s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree ??? to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users??? needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities

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Section: Key Issues In Rubiconmentioning

confidence: 99%

On the Integration of Adaptive and Interactive Robotic Smart Spaces

Dragone

Saunders

Dautenhahn

2015

Paladyn, Journal of Behavioral Robotics

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“…The second challenge is the responsibility of the Cognitive Layer (Ray et al, 2012;Leng et al, 2013). A distinct feature of the RUBICON's Cognitive Layer is that it does not analyse sensor data directly, but it reasons over the output of the Learning Layer.…”

Section: The Rubicon Architecturementioning

confidence: 99%

“…This is implemented through a MySQL database and allows interaction between the layers via PEIS middleware. The two primary components of the Cognitive Layer (Ray et al, 2012;Leng et al, 2013), which utilise this information, are the reasoning module, based on a self-organising fuzzy neural network (SOFNN), and the decision module, based on a Type-2 fuzzy neural network (FNN).…”

Section: Cognitive Layermentioning

confidence: 99%

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Dragone

Amato

Bacciu

et al. 2015

Engineering Applications of Artificial Intelligence

Self Cite

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a b s t r a c tRobotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.

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“…The developed SOFNN has the ability to adapt its neuronal structure through adding and pruning of neurons according to the incoming data. This facilitates the compactness of the computational structure and makes it suitable for real-time operation [75], [78]. The rules of the SOFNN explore the relations of the inputs and the desired reasoning outputs.…”

Section: Cognitive Layermentioning

confidence: 99%

“…To this end, RUBICON built its cognitive system over self-organising fuzzy neural networks (SOFNN) for cognitive reasoning within a smart home environment [75,76].…”

Section: Cognitive Architecturesmentioning

confidence: 99%

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Amato

Bacciu

Broxvall

et al. 2015

J Intell Robot Syst

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Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agentbased control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

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Development of Cognitive Capabilities for Smart Home using a Self-Organizing Fuzzy Neural Network

Cited by 8 publications

References 14 publications

On the Integration of Adaptive and Interactive Robotic Smart Spaces

On the Integration of Adaptive and Interactive Robotic Smart Spaces

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Robotic Ubiquitous Cognitive Ecology for Smart Homes

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