Next Article in Journal
The Healing Effect of Human Milk Fat Globule-EGF Factor 8 Protein (MFG-E8) in A Rat Model of Parkinson’s Disease
Previous Article in Journal
Lateralized Brainstem and Cervical Spinal Cord Responses to Aversive Sounds: A Spinal fMRI Study
Previous Article in Special Issue
A 20-Questions-Based Binary Spelling Interface for Communication Systems
Article Menu

Export Article

Open AccessArticle
Brain Sci. 2018, 8(9), 166; https://doi.org/10.3390/brainsci8090166

A Motivational Model of BCI-Controlled Heuristic Search

Department of Computing and Information Systems, University of Greenwich, London SE10 9LS, UK
Received: 11 June 2018 / Revised: 12 August 2018 / Accepted: 17 August 2018 / Published: 31 August 2018
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
Full-Text   |   PDF [1550 KB, uploaded 31 August 2018]   |  

Abstract

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems. View Full-Text
Keywords: augmented cognition; brain–computer interfaces; superintelligence; heuristic search augmented cognition; brain–computer interfaces; superintelligence; heuristic search
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Cavazza, M. A Motivational Model of BCI-Controlled Heuristic Search. Brain Sci. 2018, 8, 166.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Brain Sci. EISSN 2076-3425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top