This study discusses the application of sequential decision making under uncertainty and mixed observability in a mixed-initiative robotic target search application. In such a robotic mission, two agents, a ground robot and a human operator, must collaborate to reach a common goal using, each in turn, their recognized skills. The originality of the work relies in considering that the human operator is not a providential agent when the robot fails. Using the data from previous experiments, a Mixed Observability Markov Decision Process (MOMDP) model was designed, which allows to consider aleatory failure events and the partial observable human operator's state while planning for a long-term horizon. Results show that the collaborative system was in general able to successfully complete or terminate the mission, even when many simultaneous sensors, devices and operators failures happened. So, the mixed-initiative framework highlighted in this study shows the relevancy of taking into account the cognitive state of the operator, which permits to compute a policy for the sequential decision problem which prevents to re-planning when unexpected (but known) events occurs.

Decision making is a critical issue for humans operating unmanned vehicles. However, it is well admitted that many cognitive biases affect human judgments, leading to suboptimal or irrational decisions. The framing effect is a typical cognitive bias causing people to react differently depending on the context, the probability of the outcomes and how the problem is presented (loss vs. gain). There is a need to better understand the effects of these biases in operational contexts to optimize human-robot interactions. We therefore conducted an experiment involving a framing paradigm in a search and rescue mission (earthquake) and in a Mars rock sampling mission. We manipulated the framing (positive vs. negative) and the probability of the outcomes. Our findings revealed that the way the problem was presented (positively or negatively framed) and the emotional commitment (saving lives vs. collecting the good rock) statistically affected the choices made by the human operators.

The aim of this work is to predict human operator’s (HO) decisions in a specific operational context, such as a cooperative human-robot mission, by approximating his/her utility function based on prospect theory (PT). To this aim, a within-subject experiment was designed in which the HO has to decide with limited time and incomplete information. This experiment also involved a framing effect paradigm, a typical cognitive bias causing people to react differently depending on the context. Such an experiment allowed to acquire data concerning the HO’s decisions in two different mission scenarios: search and rescue and Mars rock sampling. The framing was manipulated (e.g. positive vs. negative) and the probability of the outcomes causing people to react differently depending on the context. Statistical results observed for this experiment supported the hypothesis that the way the problem was presented (positively or negatively framed) and the emotional commitment affected the HO’s decisions. Thus, based on the collected data, the present work is willed to propose: (i) a formal approximation of the HO’s utility function founded on the prospect theory and (ii) a model used to predict the HO’s decisions based on the economics approach of multi-dimensional consumption bundle and PT. The obtained results, in terms of utility function fit and prediction accuracy, are promising and show that similar modeling and prediction method should be taken into account when an intelligent cybernetic system drives human–robot interaction. The advantage of predicting the HO’s decision, in this operational context, is to anticipate his/her decision, given the way a question is framed to the HO. Such a predictor lays the foundation for the development of a decision-making system capable of choosing how to present the information to the operator while expecting to align his/her decision with the given operational guideline.