Automated software module reconfiguration through the use of artificial intelligence planning techniques

Abstract

One important approach to enhancing software re-use is through the creation of large-scale software libraries. By modularising functionality, many complex specialised 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 to experts. An approach is described to enable 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 accomplished, instead of 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. Three such systems developed by the Artificial Intelligence Group of the Jet Propulsion Laboratory and described. The multimission VICAR planner (MVP) was deployed in 1994 and used to support image processing for science product generation for the Galileo mission. MVP reduced the time for filling certain classes of requests from 4 h to 15 min. The automated SAR image processing system (ASIP) was deployed in 1996 to the Department of Geology at Arizona State University to support aeolian science analysis of synthetic aperture radar images. ASIP reduces the number of manual inputs in science product generation tenfold. Finally, the DPLAN system reconfigures software modules that control complex antenna hardware in configuring antennas to support a wide range of tracks for NASA's Deep Space Network of communications and radio science antennas.