Multi-resolutional knowledge representation using prototypes and properties

In the course of developing a distributed logistics command and control application based on the Cougaar agent architecture ,we were faced with a large knowledge representation problem. The logistics agents needed to reason about many thousands of different types of logistics assets, where each asset had hundreds of attributes. The problem is factorable, however, in that each specialized logistics agent needs to know only a relatively small amount of information about only a relatively small number of assets. By using techniques of prototypes and delegation, adapting the paradigm suggested by Lieberman (1986) and others [L. A. Stein et al., (1988)], [A. Taivalsaari, (1997)] we developed a logical data model (the Cougaar LDM) for logistics that effectively factors this representation problem. This factoring provides a basis for multiresolutional representations of the entities in the logistics system; the information about any entity at each logistics agent can be limited to only that subset in which the agent has interest.