Validation of cognitive models for collaborative hybrid systems with discrete human input

We present a method to validate a cognitive model, based on the cognitive architecture ACT-R, in dynamic human-automation systems with discrete human input. We are inspired by the general problem of K-choice games as a proxy for many decision making applications in dynamical systems. We model the human as a Markovian controller based on gathered experimental data, that is, a non-deterministic control input with known likelihoods of control actions associated with certain configurations of the state-space. We use reachability analysis to predict the outcome of the resulting discrete-time stochastic hybrid system, in which the outcome is defined as a function of the system trajectory. We suggest that the resulting expected outcomes can be used to validate the cognitive model against actual human subject data. We apply our method to a two-choice game in which the human is tasked with maximizing net coverage of a robotic swarm that can operate under rendezvous or deployment dynamics. We validate the corresponding ACTR cognitive model generated with the data from eight human subjects. The novelty of this work is (1) a method to compute expected outcome in a hybrid dynamical system with a Markov chain model of the human's discrete choice, and (2) application of this method to validation of cognitive models with a database of actual human subject data.