Hierarchical agent control

摘要

The Hierarchical Agent Control Architecture (HAC) is a general toolkit for specifying an agent's behavior. HAC supports action abstraction, resource management, sensor integration, and is well suited to controlling large numbers of agents in dynamic environments. It relies on three hierarchies: action, sensor, and context. The action hierarchy controls the agent's behavior. It is organized around tasks to be accomplished, not the agents themselves. This facilitates the integration of multi-agent actions and planning into the architecture. The sensor hierarchy provides a principled means for structuring the complexity of reading and transforming sensor information. Each level of the hierarchy integrates the data coming in from the environment into conceptual chunks appropriate for use by actions at this level. Actions and sensors are written using the same formalism. The context hierarchy is a hierarchy of goals. In addition to their primary goals, most actions are operating within a set of implicit assumptions. These assumptions are made explicit through the context hierarchy. We have developed a planner, GRASP, implemented within HAC, which is capable of resolving multiple goals in real time.HAC was intended to have wide applicability. It has been used to control agents in commercial computer games and physical robots. Our primary application domain is a simulator of land-based military engagements called “Capture the Flag.” HAC's simulation substrate models physics at an abstract level. HAC supports any domain in which behaviors can be reduced to a small set of primitive effectors such as {\sc move} and {\sc apply-force}. At this time defining agent behavior requires Lisp programming skills; we are moving towards more graphical programming languages.