Adversarial construction as a potential solution to the experiment design problem in large task spaces
This addresses the experiment design problem in large task spaces for researchers in cognitive science and AI, though it is incremental as it builds on existing methods.
The paper tackles the problem of exploring large task spaces for human behavior modeling by proposing adversarial construction to identify tasks that elicit novel behavior, showing it significantly outperforms random sampling.
Despite decades of work, we still lack a robust, task-general theory of human behavior even in the simplest domains. In this paper we tackle the generality problem head-on, by aiming to develop a unified model for all tasks embedded in a task-space. In particular we consider the space of binary sequence prediction tasks where the observations are generated by the space parameterized by hidden Markov models (HMM). As the space of tasks is large, experimental exploration of the entire space is infeasible. To solve this problem we propose the adversarial construction approach, which helps identify tasks that are most likely to elicit a qualitatively novel behavior. Our results suggest that adversarial construction significantly outperforms random sampling of environments and therefore could be used as a proxy for optimal experimental design in high-dimensional task spaces.