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The Subsumption Architecture is a special case of behavior based control for robotics. Behavioral modules are added as "layers" with each layer performing a complete behavior. Higher level behaviors override lower level ones by taking control of their effectors or manipulating their internal states. The control layers are built up out offinite state machines connected by links that act essentially like wires. To test this architecture on a reasonably complex problem, a prototype airplane controller was developed. This controller jlies a simulated aircraft from take-off to landing and was run on a "C" based implementation of the subsumption architecture. Several lessons where learned from this effort. The subsumption architecture as currently defined is not sufiiently modular. A clean interface between different behaviors would be desirable. And finally, a more general relationship than strict hierarchy between high level and low level modules is required. None of these problems is insoluble within the behavior based approach but all must be solved i f realistic problems are to be dealt with. Some candidate solutions are given.

Maritime assets such as ports, harbors, and vessels are vulnerable to a variety of near-shore threats such as small-boat attacks. Currently, such vulnerabilities are addressed predominantly by watchstanders and manual video surveillance, which is manpower intensive. Automatic maritime video surveillance techniques are being introduced to reduce manpower costs, but they have limited functionality and performance. For example, they only detect simple events such as perimeter breaches and cannot predict emerging threats. They also generate too many false alerts and cannot explain their reasoning. To overcome these limitations, we are developing the Maritime Activity Analysis Workbench (MAAW), which will be a mixed-initiative real-time maritime video surveillance tool that uses an integrated supervised machine learning approach to label independent and coordinated maritime activities. It uses the same information to predict anomalous behavior and explain its reasoning; this is an important capability for watchstander training and for collecting performance feedback. In this paper, we describe MAAW's functional architecture, which includes the following pipeline of components: (1) a video acquisition and preprocessing component that detects and tracks vessels in video images, (2) a vessel categorization and activity labeling component that uses standard and relational supervised machine learning methods to label maritime activities, and (3) an ontology-guided vessel and maritime activity annotator to enable subject matter experts (e.g., watchstanders) to provide feedback and supervision to the system. We report our findings from a preliminary system evaluation on river traffic video.