TY - JOUR
T1 - BCI Control of Heuristic Search Algorithms
AU - Cavazza, Marc
AU - Aranyi, Gabor
AU - Charles, Fred
PY - 2017/1/31
Y1 - 2017/1/31
N2 - 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.
AB - 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.
U2 - 10.3389/fninf.2017.00006
DO - 10.3389/fninf.2017.00006
M3 - Article
SN - 1662-5196
SP - -
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
ER -