Abstract
A key obstacle hampering the fielding of AI planning applications is the considerable expense of developing, verifying, updating and maintaining the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. Consequently, in order to field real systems, planning practitioners must be able to provide (1) tools to allow domain experts to create and debug their own planning knowledge bases; (2) tools for software verification, validation and testing and (3) tools to facilitate updates and maintenance of the planning knowledge base. This paper begins by describing a planning application of automated image processing and our overall approach to knowledge acquisition for this application. This paper then describes two types of tools for planning knowledge-base development: static KB analysis techniques to detect certain classes of syntactic errors in a planning knowledge-base and completion analysis techniques to interactively debug the planning knowledge base. We describe these knowledge development tools and describe empirical results documenting the usefulness of these tools.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call