Tactical Intelligence Tools for Distributed Agile Control of Air Operations

This paper presents an innovative architecture for engineering an agile distributed system of interacting agents, which is modeled as a set of interacting automata[11]. By slight modification of Goranson's definition of agility [5], we define agile control as the collective ability of agents to continually adapt to expected and unexpected changes. The intelligent control of the enterprise, then, consists of (i) continual observation of events and identification of relevant changes on the battlefield (ii) controllers implementing a dynamic C strategy as a control specification for each agent in the enterprise, and (iii) tactical intelligence tools which process necessary transient and situational information for the agents to execute the control decisions in the battlefield. A testbed implementing these components and their interactions has been established as shown in Figure 1. Methods for engineering agile control specifications for a hierarchy of agents in the air operations enterprise are discussed in a companion paper in these proceedings [14]. This paper presents the overall architecture and its testbed implementation for agile control of air operations. In addition, a variety of tactical intelligence tools are discussed which provide situational and transient information for executing control decisions. Algorithms for dynamic assignment of targets to aircraft is an example of a tactical intelligence tool. These algorithms require current and situational knowledge of resources and positions of identified targets to continually reconfigure assignments. Both centralized and onboard tactical intelligence algorithms are discussed and compared for relative performance. The testbed is being used for more detailed experimental verification of C strategies for air operations as described in another companion paper in these proceedings [4]. 1. Intelligent Control of Dynamic Operations The air operations enterprise can be represented as a dynamic hierarchy of intelligent agents who change their internal states in response to interactions with other agents or the environment. The dynamic time-evolution of complex interactions of agents in the enterprise is inherently different from the continuous variable dynamics of physical processes. Unlike the evolution of physical processes which are constrained by physical laws, agent interactions are triggered by discrete events and are characterized by a large number of interacting processes under predictable and unpredictable disturbances. Hence there are no inherent physical laws to constrain system configurations other than natural limitations of human/machine comprehension, resources and ergonomics. Therefore, an accurate plant model, based on physical laws, cannot easily be formulated. Concurrent dynamic processes, embedded at each node of the system, interact in highly non-linear, time-varying stochastic and possibly inconsistent ways. Hence model based conventional control methods are inadequate. Alternate methods of designing controllers whose structure and outputs are determined by empirical evidence through observed input/output behavior rather than by reference to a plant model are necessary. Several techniques for such non-linear controller design have recently been proposed in recent literature on Intelligent Control [6, 7, 10, 2, 9, 12]. Meystel [8] has proposed a nested hierarchical control architecture for the design of Intelligent Controllers. Albus [2] has also developed the Real Time Control Architecture in which sensor and processing, value judgment, world modeling and behavior generation subsystems interact to adaptively generate appropriate response behaviors to sensor observations and knowledge of mission goals. Brooks' subsumption architecture [3] for intelligent control is based on achieving increasing pre-specified levels of competence in an intelligent system by examining outputs of lower levels. An innovative behavior based architecture for intelligent agile control of agents in the air operations enterprise is presented here. The mathematical representation of agent interactions as a cellular space of interacting automata was formulated in [11]. The uniqueness of this approach is that it allows a heterogeneous group of agents to self organize and coordinate composite behaviors to support the execution of desired global behaviors of the system. Desired system behaviors are enabled through the hierarchical control architecture of the system and represented as control specifications. Methods for the control specifications for an agent hierarchy and for checking the controllability and hierarchical consistency of the resulting system are discussed in [14]. In section 2 we present an overall architecture for implementing agile enterprise control. Section 3 discusses centralized versus distributed tactical intelligence. In Section 4, we present four different algorithms for dynamic target scheduling and routing of aircraft in a limited SEAD Scenario [13] for corridor clearance. Section 5 discusses resource bounded optimization and performance comparisons for the four algorithms. More detailed experimental verification of distributed C strategies on this testbed is discussed in [4]. 2. Agile Control Architecture The essential components of Agile Control are shown in Figure 1. These are: (i) Identification and communication of relevant change in the enterprise to appropriate agents. (ii) Hierarchical controller for specifying and implementing a control strategy, and (iii) Tactical intelligence tools for providing intelligent decision support for executing the control strategy. The description and interaction of these components will be described in detail in this paper. An enterprise simulator, representing air operations in the battlefield, generates events which are observed and responded to by intelligent agents. The design of the hierarchical controller assumes the strategic knowledge an experienced commander may have in specifying a control strategy for his agents for a particular mission. For example, in the corridor clearing scenario described later in this paper, the control strategy will be based on mission planning information which may not be specific in terms of the exact location of targets. In general, sufficient planning information is assumed so that our overall control strategy can be implemented. Control specifications define the execution level behavior-modification rules for each agent in response to observed changes in the enterprise. These specifications are ill-posed, however, for the execution level dynamics Fig 1. Components of the Agile Control Architecture Hierarchical Controller Enterprise Simulator) Tactical Intelligence Tools