Coordination , authority trading , and control in the context of safety-critical systems

It is a frequently stated fact in the field of human-computer interaction that especially with increasingly networked socio-technical systems, the focus of analysis and design should be on multi-agent systems, comprising a number of human and artificial agents, instead of on single human-computer systems (Boy, 2002). However, current methods tend still to be more applicable to single human-computer systems, with one of the major problems for the analysis and design of multi-agent systems being the dramatically increased complexity. In the following, some general guidelines for the analysis and design of multi-agent systems are developed based on the rationale of two existing methods, Cognitive Function Analysis (Boy, 1998) and KOMPASS (Grote et al., 2000; Wäfler et al., 2003), using multi-agent teams in aviation (pilots and air traffic control) as examples. The aspects of coordination, authority trading, and control in the context of safety-critical systems are discussed, as they represent crucial problems in multi-agent systems that have not yet received sufficient attention in the HCI field. 1 Autonomy and coordination A system that is to perform tasks does so on the basis of some form of division of labor. Any chosen division of labor involves a particular pattern of specialization for each of the actors in the system and at the same time requires coordination of the specialized actors, which is contingent on the degree and kind of specialization (e.g. Kieser & Kubicek, 1989). Crucial in this respect is the type of interdependence created by the chosen division of labor in combination with the general demands of the task and the task environment. Generally, three types of interdependence of work activities are distinguished (e.g. Thompson, 1967; van de Ven et al., 1976): • Pooled interdependence is present when system performance is an additive function of individual performance. The performance of other members of the system can have an effect on the work of the individual members but only indirectly through parallel contributions to a superordinate goal. Coordination in this case is usually achieved via centrally determined work programs which every individual has to follow independently and which assure that subtasks serve the superordinate goal. • Sequential interdependence is a unidirectional workflow arrangement, where individual performance depends on the proper fulfillment of prior subtasks. Synchronization is needed here based on centrally determined programs and plans, which spell out the exact content and temporal requirements of subtask fulfillment. • Reciprocal interdependence means that information and results of work activities have to be exchanged between teams continuously. Coordination is mainly achieved via direct communication, be it in the form of personal directives or multilateral flow of communication between the individuals involved in self-regulated task performance. In view of special demands created by task performance in high risk situations like flying an airplane or operating on a patient, Tesluk et al. (1997) have added a fourth form of interdependence called intensive work situations where team members work very closely together and work flow is poly-directional, flexible and very intensive, because the team repeatedly faces novel situations with new problems which have to be diagnosed and solved within the team. This is also similar to what Van de Ven et al. (1976) have termed “team arrangement” where coordination has to be achieved by all group members simultaneously. Proceedings of HCI International 2005 – Las Vegas, USA In the following, the focus will be on such intensive work situations, though certain parts of task execution might follow more one of the other forms of task interdependence. In the aviation system as a prime example of this type of work situation, not only do pilots and controllers interact with each other in complex and time pressured situations, but they also interact with several highly complex technical systems. Automation is not a new phenomenon in aviation. The first autopilots on transport aircraft were used in operations in the 1930s. Aircraft automation has developed since then from analogical systems to digital computers that include flight management systems (FMS), ground collision avoidance systems (GCAS) or communication management systems (DataLink). These incremental evolutions deeply changed human-system as well as human-human relations in the aviation system: pilots have become managers of artificial agents (i.e., highly automated systems) that are in charge of basic flight functions such as trajectory tracking, navigation, communications, collision avoidance, etc. Agent management and supervision involve more socio-cognitive activities. Even if this technological evolution has improved performance, safety and comfort within the overall aviation system, it was also induced a number of human factors issues that are now well identified such as effective responsibility distribution and sharing, control skill erosion, monitoring-induced workload, and situation awareness. In addition to the nature of the task itself, characteristics of the environment in which it has to be carried out are very important when choosing the division of labor along with the appropriate form of coordination. In order to understand the reasons for and effects of different forms of coordination such as impersonal coordination (through standards and programs), vertical personal coordination through leadership and lateral personal coordination through interaction between team members (Van de Ven et al., 1976), it is helpful to conceptualize organizational activities in terms of the management of uncertainty (Grote, 2004a). The kinds of uncertainty an organization has to deal with and how these uncertainties are handled by the organization has been a core issue in organization theory. Prominent authors in this field such as Thompson (1967), Perrow (1967) and Van de Ven et al. (1976) have helped to systematize the nature of uncertainties relevant to organizations and the ways organizations deal with them. Two general sources of uncertainties were identified, i.e. the transformation processes an organization has to perform and the environment within which these processes take place. There are two extreme approaches to handling uncertainty (see Figure 1). The first one tries to minimize uncertainty or at least the effects of uncertainty in the organization using mainly feed-forward control based on high standardization and programming of work flows. Enormous efforts are put into centralized planning and continuous monitoring of the execution of these plans, providing minimal degrees of freedom to the people in charge of carrying out the plans. If the organization’s environments become more and more complex – or rather the complexity is more and more acknowledged – then more effort has to be put into reducing the uncertainties connected with the complexity. The minimizing uncertainties approach is especially prevalent in organizations where malfunctioning not only endangers business success but also human life and environmental integrity because the production processes handled incorporate major risks for health, safety and environment. In these organizations usually not only the chosen management style is that of minimizing uncertainties, but this approach is even prescribed by external regulatory bodies. Minimizing uncertainties • complex, central planning systems • reducing operative degrees of freedom through procedures and automation • disturbances as to be avoided symptoms of inefficient system design Coping with uncertainties • planning as resource for situated action • maximizing operative degrees of freedom through complete tasks and lateral cooperation • disturbances as opportunity for use and development of competencies and for system change