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 putti ng together a complex language processing and reasoning agent. The sys tem is intended as an experimental prototype of an intelligent, conver sationally proficient planning advisor in a dynamic domain of cargo tr ains and factories. We explain some of the goals and particulars of th e KRs used, evaluate the extent to which they served their purposes, a nd point out some of the tensions between representations that needed to be resolved. On the whole, we found that using very expressive repr esentations minimized the tensions, since it is easier to extract what one needs from an elaborate representation retaining all semantic nua nces, than to make up for lost information.