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BCI Control of Heuristic Search Algorithms

Cavazza, Marc, Aranyi, Gabor, Charles, Fred (2017) BCI Control of Heuristic Search Algorithms. Frontiers in Neuroinformatics, 11 . p. 6. ISSN 1662-5196. E-ISSN 1662-453X. (doi:10.3389/fninf.2017.00006)

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.

Item Type: Article
DOI/Identification number: 10.3389/fninf.2017.00006
Uncontrolled keywords: brain-computer interfaces (BCI), neurofeedback (NF), functional near-infrared spectroscopy (fNIRS), heuristic search
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Marc Cavazza
Date Deposited: 31 Jan 2017 10:21 UTC
Last Modified: 01 Aug 2019 10:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60139 (The current URI for this page, for reference purposes)
Cavazza, Marc: https://orcid.org/0000-0001-6113-9696
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