The 4D/RCS conceptual framework demonstrates how intelligent unmanned vehicle systems can be integrated into any military command and control structure.

Df. A framework is a description of the functional elements, the representation of knowledge, and the flow of information within the system.

4D/RCS integrates the functional elements, knowledge representations, and flow of information so that intelligent systems can analyze the past, perceive the present, and plan for the future. It enables systems to assess the cost, risk, and benefit of past events and future plans, and to make intelligent choices among alternative courses of action.

The 4D/RCS is a hybrid architecture, with both deliberative (reasoning and planning) and reactive (rapid response to exigencies) capabilities. At every level of the control hierarchy there are deliberative planning processes that receive goals and priorities from superiors and decompose them into subgoals and priorities for subordinates at levels below. At every level, reactive loops respond quickly to feedback to modify planned actions so that goals are accomplished despite unexpected events. Thus, planning and decision making are distributed throughout the hierarchy. At every level, plans are formulated, decisions are made, and reactive actions are taken locally by the units that are most affected and best able to analyze the situation and respond effectively.

At every level, sensory processing filters and processes information derived from observations by subordinate levels. Events are detected, objects recognized, situations analyzed, and status reported to superiors at the next higher level.

At every level, sensory processing and behavior generation processes have access to a model of the world that is resident in a knowledge database. This world model enables the intelligent system to analyze the past, plan for the future, and perceive sensory information in the context of expectations.

At every level, a set of cost functions enable value judgments and determine priorities that support intelligent decision making, planning, and situation analysis. This provides a robust form of value driven behavior that can function effectively in an environment filled with uncertainties and unpredictable events. It assures that intelligent vehicle systems will always obey direct orders from human commanders, and when out of contact, will respect the goals, priorities, and rules of engagement set by human commanders.

The 4D/RCS architecture maps naturally onto the military command and control structure. At the top level of any military hierarchy, there is a human commander supported by a staff that provides intelligence and decision support functions. This is where high-level strategy is defined and strategic goals are established. The top level commander decides what kind of operations will be conducted, what rules of engagement will be followed, and what values will determine priorities and shape tactical decisions.

Throughout the hierarchy, strategic goals are decomposed through a chain of commands that consists of operational units made up of intelligent agents (humans or machines), each of which possesses a particular combination of knowledge, skills, and abilities, and each of which has a well-defined set of duties and responsibilities. Each operational unit accepts tasks from a higher level unit and issues sub-tasks to subordinate units. Within each operational unit, intelligent agents are given job assignments and allocated resources with which to carry out their assignments; the intelligent agents then schedule their activities so as to achieve the goals of the jobs assigned to them. Each agent is expected to make local executive decisions to achieve goals on schedule by solving local problems and compensating for local unexpected events. Within a unit, each agent acts as a member of a team in planning and coordinating with peers at the same level, while simultaneously acting as the commander of a subordinate unit at the next lower level.

Each agent, within each operational unit, has knowledge of the world environment in which it must function. This knowledge includes state variables, maps, images, and symbolic descriptions of the state of the world. It also includes knowledge of objects and groups that exist in the environment, including their attributes and relationships, and knowledge of events and processes which develop over time. Knowledge is kept current and accurate through sensors and sensory processing systems that detect events and compute attributes of objects and situations in the world. Knowledge of the world also includes laws of nature that describe how the environment behaves under various conditions, as well as values and cost functions that can be used to evaluate the state of the world and the performance of the intelligent control system itself.

At the bottom of the hierarchy, the system performs physical actions (e.g., the movement of effectors such as wheels, tracks, arms, legs, thrusters, or control surfaces), which affect the environment. Simultaneously, sensors measure phenomena - including the effects of the system itself - in the environment. This process is a continuous loop of the environment affecting the robotic system and the robotic system affecting the environment.

For any chain of commands, an organizational chart can be constructed that describes functional groupings and defines who reports to whom. However, organizational charts typically do not show all the communication pathways by which information flows throughout the organization. In particular, much information flows horizontally between agents and operational units, through both formal and informal channels. Multiple agents within operational units share knowledge about objects and events in the world, and status of other agents. For example, agents operating on the battlefield often can see each other and may respond to requests for help from peers without explicit orders from superiors. Also, plans developed in one operational unit may be communicated to other units for implementation.

4D/RCS explicitly allows for the exchange of information between organizational units and agents at the same level or different levels. Commands and status reports flow only between supervisor and subordinates, but queries, replies, requests, and broadcasting of information - by posting in common memory or by messaging mechanisms - may be used to convey information between any of the units or agents in the entire 4D/RCS architecture.

The 4D/RCS organizational chain of commands is defined by the duties and responsibilities of the various organizational units and agents and by the flow of commands and status reports between them, not by access to information or the ability to communicate. This means that while the relationships between supervisors and subordinates is in the form of a tree, the exchange of information between units and agents is a graph that, in principle, could be fully connected. In practice, however, the communication network is typically not fully connected because many of the units and agents simply have nothing to say to each other.

4D/RCS also explicitly allows the organizational hierarchy to be dynamically reconfigured in real-time during operation. This permits the organizational chain of commands to change over time as units are added or removed from the chain of commands, or moved from one place to another in the organization. For example, a wing of unmanned aircraft may be under the control of a base flight controller during take-off or landing, be handed off to a route flight controller, and be transferred to a combat engagement controller during attack and battle damage assessment. Similarly, unmanned ground vehicles may be transferred from one chain of commands to another as conditions change on the battlefield. Individual agents may be promoted or replaced, and units reconfigured during combat operations to compensate for casualties.