Knowledge representation in the TRAINS-93 conversation system

We describe the goals, architecture, and functioning of the TRAINS-93 system, with emphasis on the representational issues involved in putting together a complex language processing and reasoning agent. The system is intended as an experimental prototype of an intelligent, conversationally proficient planning advisor in a dynamic domain of cargo trains and factories. We explain some of the goals and particulars of the KRs used, evaluate the extent to which they served their purposes, and point out some of the tensions between representations that needed to be resolved. On the whole, we found that using very expressive representations minimized the tensions, since it is easier to extract what one needs from an elaborate representation retaining all semantic nuances, than to make up for lost information.