KATE, a highly-simplified stick figure mannequin that can execute OMAR tasks. Many of the architectures can be extended by building custom motor modules.
Most of the architectures generate outputs in the form of behaviors of the human being represented (e.g., motions, gestures, utterances) that can or could be used as inputs to other simulation modules, such as tank or aircraft models. HOS (in its original version) and Micro Saint, however, generate performance metrics, such as task start and end times and task accuracy data. The former are more applicable for virtual simulations, which need to generate visible images of human and system behaviors, while the latter are more applicable to constructive simulations. In principle, however, it should be possible to modify the two systems to produce such behaviors; in fact, as discussed earlier, modified HOS micro-models have recently been added to COGNET for that very purpose.
Representation of declarative knowledge ranges from simple variables to complex frames and schemas. Those applications in which complex factual knowledge structures must be represented explicitly would be better served by the more sophisticated techniques of such architectures as ACT-R and OMAR.
All the architectures, except neural net architectures, have a means of representing procedural knowledge beyond that provided by the languages in which they are written. The production rule languages of ACT-R and Soar appear to be the most flexible and powerful, though the related complexity of such languages may not be warranted for those applications in which behavior is highly procedural (perhaps even to the point of being scripted).
Only three architectures offer learning: ACT-R, neural net architectures, and Soar. Of these, ACT-R provides the most flexibility.
In most of the architectures, situation assessment is overt, in the sense that