Skip navigation

A motivational model of BCI-controlled heuristic search

A motivational model of BCI-controlled heuristic search

Cavazza, Marc ORCID: 0000-0001-6113-9696 (2018) A motivational model of BCI-controlled heuristic search. Brain Sciences, 8 (9):166. ISSN 2076-3425 (Online) (doi:

PDF (Publisher's PDF - Open Access)
21430 CAVAZZA_Motivational_Model_of_BCI-Controlled_Heuristic_Search_(OA)_2018.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview


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.

Item Type: Article
Additional Information: © 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Uncontrolled Keywords: augmented cognition; brain–computer interfaces; superintelligence; heuristic search
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 22:34
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics