Human-machine interactions constitute a special case of distributed intelligence. We have an ongoing research program addressing a variety of issues concerning the distribution of tasks and responsibilities between human operators and complex machines.An early product of this research, the simulation model MESSAGE, addressed transfer and processing of information in aircraft cockpits. This system enables the comparison of cockpits or procedures for given flight management tasks. Workload and performance indices are computed from information flow and content. In MESSAGE, there are several information processing systems (IPSs), which can be human or machine, communicating and cooperating through a common information database or “blackboard”. The research centered on analyses of the structure of the messages exchanged between IPSs and the supervisory function of each IPS in managing these messages. The critical features of the system were found to be the interface filter and the ability of each IPS to cope with parallel and sequential processing. Humans have been shown to be very good parallel processors for highly learned tasks. Conversely, they handle new or non-common situations sequentially.Recent investigations, carried out in cooperation with NASA, examined the effects of different distribution of autonomy between humans and machines on the performance of complex human-machine systems. These issues were investigated in an application for fault diagnosis in an orbital system for refuelling satellites. A cooperative expert system, HORSES, was developed and used in a series of experiments with naive and experienced operators diagnosing faults. It was found that the division of autonomy needs to be modified as the human operator learns to use the system. It was shown that humans can be helped by intelligent tools which are self-explanatory. Other research suggests that if the human cannot understand the whole system, he tends to become too reliant on automatic aids and to lose overall control of the system. Indeed, humans tend to be poor in pure monitoring, however given some responsibility for action, their monitoring function can improve. One solution is total automation, however if a human is to be included, intelligent tools can be used to monitor his performance and offer assistance, particularly in routine operations. This suggests that distributed intelligence systems could be used to “observe” what other IPSs (including human operators) are doing, In order to avoid undesired situations.This previous work has led to the concept of ”Operator Assistant” (OA) systems which are IPSs that simultaneously monitor the environment and the operator(s). An OA system includes a situation recognizer, a diagnoser, a planner and controller. Each module works on multiple knowledge bases able to handle information coming from the environment, including the operator(s).