Using Artificial Intelligence Planning Techniques to Automatically Reconfigure Software Modules

One important approach to enhancing software re-use is through the creation of large-scale software libraries. By modularizing functionality, many complex specialized applications can be built up from smaller reusable general purpose libraries. Consequently, many large software libraries have been formed for applications such as image processing and data analysis. However, knowing the requirements and formats of each of these routines requires considerable expertise thus limiting the usage of these libraries by novices. This paper describes an approach to allowing novices to use,complex software libraries. In this approach, the interactions between and requirements of the software modules are represented in a declarative language based on Artificial Intelligence (AI) Planning techniques. The user is then able to specify their goals in terms of this language designating what they want done, not how to do it. The AI planning system then uses this model of the available subroutines to compose a domain specific script to fulfill the user request. Specifically, we overview three such systems developed by the Artificial Inteligence Grou of the Jet Propulsion Laboratory. The Multimission VICAR Planner (MVP) has been deployed for 2 years and supports image processing for science product generation for the Galileo mission. MVP has reduced time to fill certain classes of requests from 4 hours to 15 minutes. The Automated SAR Image Processing system (ASIP) which is currently in use by the Dept. of Geology at ASU supporting aeolian science analysis of synthetic aperture radar images. ASIP reduces the number of manual inputs in science product generation by 10-fold. Finally, the DPLAN system reconfigures software modules which control complex antenna hardware to configure antennas to support a wide range of tracks for NASA's Deep Space Network of communications and radio science antennas.