Knowledge Representation in the TRAINs-93 Conversation System.
Abstract
We describe the goals, architecture, and functioning of the TRAIN 5-93 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. For this team effort, our strategy at the outset was to let the designers of the various language processing, discourse processing, plan reasoning, execution and monitoring modules choose whatever representations seemed best suited for their tasks, but with the constraint that all should strive for principled, general approaches. Disparities between modules were bridged by careful design of the interfaces, based on regular in-depth discussion of issues encountered by the participants. Because of the goal of generality and principled representation, the multiple representations ended up with a good deal in common (for instance, the use of explicit event variables and the ability to refer to complex abstract objects such as plans); and future unifications seem quite possible. 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1996
- Accession Number
- ADA329872
Entities
People
- David R Traum
- Lenhart K. Schubert
- Marc Light
- Massimo Poesio
- Nathaniel G. Martin
Organizations
- University of Rochester