Two trends in electronic hardware are expected to have significant impacts on day-to-day operational practice in the military and in most other organizations that operate large numbers of resources: (i) more 'intelligence' is being embedded into smaller and cheaper devices; (ii) wireless communication is improving in availability, performance and cost. As a result of these trends, individual, small devices are becoming more capable, but even more so, collaborative networks of small devices are beginning to rival large, traditional resources in terms of capability (at least in some dimensions) and to exceed them in terms of cost-effectiveness and flexibility.
This introduction will briefly discuss a couple of motivating examples and then address one of the main problems associated with large networks of resources: how to manage the resources in a scalable, robust and cost-effective manner.
Traditionally, sensor networks have been comprised of a small number of high-quality, high-cost sensors connected to a central observation and control station using either wired communication or high-power radio. Examples include observational satellites, closed-circuit television and radar installations. While such networks continue to be valuable, the increasing availability of lower-quality, lower-cost sensors and short-range radio communication provides an alternative in which many "small" sensors collaborating replace a single, "large" sensor.
What advantages might be gained by using many smaller sensors?
There are, of course, several limitations to using small sensors and several potential problems that need to be addressed.
This last area, coordination, is the focus of the ANTs program: given a sensor network, coordinate the sensors to accurately detect and monitor features that are of interest. For example, an "feature" may be a moving object and the network may be required to detect and track objects as they move into and through its surveillance region.
Just as new generations of small sensors may be used en masse instead of traditional, large sensors, so too new generations of small actuators may be used instead of traditional, large actuators. For example, unmanned air vehicles (UAV) and, in particular, the combat variants (UCAVs) are much smaller and cheaper than manned aircraft but are capable of undertaking some of the same tasks, to a limited extent (their payloads are much smaller, for example).
Because of the limitations of a single UAV, it is useful to have UAVs operate collaboratively. Over open terrain, UAV teams may operate as small, tightly-knit squadrons with narrow goals. However, in urban environments, it may be more useful to have many UAVs operate as a loosely organized network, opportunistically collaborating as the need arises to accomplish (possibly competing) goals that are dynamically generated by many ground commanders, to surveil broad regions for hostile activity and to maintain communication infrastructure.
Under such operating circumstances, many of the same pros and cons apply as for sensor networks, although generally energy consumption tends to be much higher and a single UAV would be deployed for only a limited time before maintenance.
To summarize, the problem that is the main focus of the ANTs program may be described as follows:
In addition, the following requirements hold:
Even ignoring the communication aspects, coordinating a large number of resources is a difficult problem due to the combinatorial growth in the number of possible states of the network. Given that the ANTs program requires real-time adaptivity, it is clearly infeasible to try to attain perfect coordination. Instead, the aim is to achieve coordination that is good enough for the resource network to accomplish most of its tasks without being too inefficient, and to produce the coordination decisions quickly enough that the resource network can keep pace with a dynamic environment.
This objective was summarized in the slogan: good-enough, soon-enough.
The ANTs program also proposed a conceptual architecture for coordination, that was, in the main, respected by the various projects. Because the size of the network and the communication latency make it impossible for any one process in the network to acquire global knowledge, the coordination mechanism would be split into many processes, each operating with limited knowledge and authority.
Moreover, because the resource network itself may be dynamic (as the status of resources changes) and because the tasks that the network is to accomplish may be dynamic, the organization of the coordination processes would also be dynamic: in effect, the processes would dynamically form themselves into teams to collectively accomplish tasks.
In order for the processes to efficiently form effective teams, they would need to communicate with each other. Since, each process would likely have somewhat different information and responsibilities from the others, due to its particular location in the network, a real-time negotiation framework seemed well-suited, as it would provide mechanisms for reaching a compromise with limited effort in spite of non-uniform and possibly competing constraints and objectives.
Thus the program name: Autonomous, Negotiating Teams.
The program was organized as follows: