BCI Control of Heuristic Search Algorithms

Hdl Handle:
http://hdl.handle.net/10149/620802
Title:
BCI Control of Heuristic Search Algorithms
Authors:
Cavazza, M. (Marc); Aranyi, G. (Gabor); Charles, F. (Fred)
Affiliation:
Teesside University. Digital Futures Institute
Citation:
Cavazza, M., Aranyi, G., Charles, F. (2017) 'BCI Control of Heuristic Search Algorithms' Frontiers in Neuroinformatics; Available online: 31 Jan 2017
Publisher:
Frontiers
Journal:
Frontiers in Neuroinformatics
Issue Date:
31-Jan-2017
URI:
http://hdl.handle.net/10149/620802
DOI:
10.3389/fninf.2017.00006
Additional Links:
http://journal.frontiersin.org/article/10.3389/fninf.2017.00006/full
Abstract:
The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A (WA ) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA . Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems.
Type:
Article
Language:
en
ISSN:
1662-5196
Rights:
This is an open access article published under a Creative Commons Attribution License. For full details see https://creativecommons.org/licenses/by/4.0/ [Accessed: 01/03/2017] This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.

Full metadata record

DC FieldValue Language
dc.contributor.authorCavazza, M. (Marc)en
dc.contributor.authorAranyi, G. (Gabor)en
dc.contributor.authorCharles, F. (Fred)en
dc.date.accessioned2017-03-01T16:24:47Z-
dc.date.available2017-03-01T16:24:47Z-
dc.date.issued2017-01-31-
dc.identifier.citationFrontiers in Neuroinformatics; 11en
dc.identifier.issn1662-5196-
dc.identifier.doi10.3389/fninf.2017.00006-
dc.identifier.urihttp://hdl.handle.net/10149/620802-
dc.description.abstractThe ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A (WA ) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA . Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems.en
dc.language.isoenen
dc.publisherFrontiersen
dc.relation.urlhttp://journal.frontiersin.org/article/10.3389/fninf.2017.00006/fullen
dc.rightsThis is an open access article published under a Creative Commons Attribution License. For full details see https://creativecommons.org/licenses/by/4.0/ [Accessed: 01/03/2017] This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.en
dc.titleBCI Control of Heuristic Search Algorithmsen
dc.typeArticleen
dc.contributor.departmentTeesside University. Digital Futures Instituteen
dc.identifier.journalFrontiers in Neuroinformaticsen
or.citation.harvardCavazza, M., Aranyi, G., Charles, F. (2017) 'BCI Control of Heuristic Search Algorithms' Frontiers in Neuroinformatics; Available online: 31 Jan 2017en
dc.eprint.versionPublisher's Version/PDFen
dc.embargoNoneen
dc.date.accepted2017-01-16-
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