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.
Highlights
Brain-Computer Interfaces (BCI) have been a major component of human augmentation research (Schmorrow, 2005), in which intelligent processing was harnessed to extend human information processing abilities
In ‘‘Experiment I: BCI Control of 8-Puzzle Solving’’ Section, we describe the design of our fNIRS-based BCI system, as well as the NF protocol that has been used in two experiments, each one using a different search problem (8-puzzle and path planning)
We have presented a novel use of BCI, aiming at interfacing directly at the algorithmic level of Artificial Intelligence (AI) computations, supported by a proof of concept experiment on two traditional heuristic search benchmarks
Summary
Brain-Computer Interfaces (BCI) have been a major component of human augmentation research (Schmorrow, 2005), in which intelligent processing was harnessed to extend human information processing abilities. A new vision of heuristics has emerged within AI, one which emphasizes pruning as the most important function of heuristics, rather than providing information guiding towards an optimal solution (Sturtevant et al, 2012). This suggests that users’ cognitive attitudes could have an input at the level of heuristic functions calculation, influencing the amount of pruning associated with a heuristic. We can hypothesize that cognitive attitudes related to motivation, reward anticipation, or risk acceptance, are candidates to control heuristic search
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