Abstract:  Adaptive Function Allocation for Human-Machine Systems: a Comparative Study of Triggers, Strategies and Stabilization.

Amit Lagu
Ph.D. Candidate

Major Professor: Steven J. Landry

An adaptive allocation system is capable of dynamically reallocating functions between different agents. In adaptive allocation, the need for reallocation can be determined using different parameters (trigger mechanisms) such as fluctuation in workload or changes in physiology of the human agent. The adaptation strategy can determine the amount of reallocation and set the new level of the automated agent.  The first experiment evaluated the effects of trigger type (performance or heart rate) and adaptation strategy (complete reallocation or partial transformation) on the number of errors made. The presence of adaptive allocation reduced the number of errors. The ANOVA results show that there is an interaction effects between the trigger type and adaptation strategy. Due to the presence of an interaction effect, inferences about main effect of trigger type or adaptation strategy could not be made. The combination of partial transformation strategy and heart rate trigger mechanism resulted in substantially more (nearly 114%) errors than other combinations, which were not significantly different. Based on consistent cell means (except for the combination of partial transformation and heart rate,) it is hypothesized that there are no main effects of trigger type or strategy. The second experiment monitored the LOA for presence of stabilization. No reliable stabilization was observed within 30 minutes. Based on the contingency table analyses, there appears to be an effect of adaptation strategy on the mean LOA and the effect of trigger type on the time of onset of stabilization. Based on the post-hoc ANOVA analyses, there appears to be an effect of trigger type on number of reallocations and the probability of a change in LOA being observed.