In distributed, decentralized control applications of the kind found in
systems, it is important to be able to evaluate hierarchies and
heterarchies of control architectures for the following reasons:
If the performance degradation of a completely decentralized solution is unacceptable and a completely centralized solution is prohibitively complex or expensive, a compromise will have to be found. Such a compromise will feature semi-autonomous agent operation. In this case, each agent is trying to optimize its own usage of the resource and coordinates with ``neighboring'' agents and a base station in case there is a conflict of objectives. It should be noted that semiautonomous agent control is naturally suited for hybrid designs. At the continuous level, each agent chooses its own optimal strategy, while discrete coordination is used to resolve conflicts. Thus, the class of hybrid systems that we will be most interested in are semi-autonomous multi-agent systems, where the hybrid dynamics arise from the interaction between continuous single agent ``optimal'' strategies and discrete conflict resolution or coordination protocols.
The need for rapid and varied agent and inter-agent reconfiguration, the uncertainty of information, its localization and the inter-agent or agent-base communication required to optimize global performance in the face of such localization, result in emergent behaviors of extraordinary complexity. Once again, our experience with the design of such systems suggests that the management of complexity demands that a theory of multi-agent control embed within it a theory of hierarchical control and a corresponding theory of hierarchical abstraction of information. Needless to say, the theory must locate and manipulate within its discourse both planning and decision making layers designed by soft computing techniques as also layers that quickly and decisively translate decisions or plans into deterministic sequences of agent and inter-agent actions that are formally designed.
There is a continuum of design choices for system decomposition, ranging from strict hierarchical control to a fully distributed, multi-agent system. Furthermore, different choices may be appropriate at different levels of abstraction, ranging from the (typically continuous-domain) low-level control systems concerned with safety and smooth execution to the (typically symbolic/discrete) strategic levels concerned with optimization and planning for high-level goals. We will investigate theoretical and design issues involved in the choice of system architecture, and methods for interfacing elements of the resulting hybrid system. We will study the choice of hierarchies and heterarchies required for UCAVs. We will investigate the extent to which we can organize the control of complex systems to achieve emergent optimal behavior of the collective system for the usage of a scarce resource by many agents operating with varying degrees of autonomy.
We envision a design, simulation, and verification environment for multi-agent hierarchical hybrid control systems as shown in Figure 1.
Figure 1: Multi-Agent Hybrid System Design Environment
The specifications are described by the desired emergent behavior of the collection of agents. These requirements are usually described linguistically, such as increased safety and throughput, reduced emissions for AHS, or increased frequency of landings and takeoffs and optimum utilization of air space for Air Traffic Management Systems. These requirements should get ``compiled'' into a system architecture.
The controller of each agent is described by a multi-layer hierarchy. The higher layers are typically modeled by discrete-event systems, which plan and reason under uncertainty, and take strategic decisions in coordination with other agents. The lower layers, on the other hand, typically involve continuous dynamics and performing path planning and regulation tasks. Figure 2 illustrates such a hierarchical organization of diagnostics and control layers required for fault management of automated vehicles.
Figure 2: Fault Management Architecture
The lower layer here is comprised of the sensors and observers that process the continuous dynamics. The sensing interface, designed by soft computing techniques, reasons with the uncertainty in sensor and observer information. It is responsible for mapping sensor and observer outputs to a decision on whether the output is high or low. This crisp decision is processed by the diagnoser which is a formal entity that has a deterministic output for every sequence of inputs.
The control laws at different layers along with the inter-agent coordination schemes are to be designed, in order to satisfy the specified requirements such as safety, productivity, efficient resource utilization, etc. Tools used for design may be conventional discrete / continuous / hybrid tools. Once the control laws for individual agents are designed, the various layers can be formalized as collections of hybrid modules. This will allow micro-simulation of individual agents and macro-simulation of the collective emergent behavior, and support attempts at both micro- and macro-verification of crucial system characteristics.