SHINE Expert System

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Spacecraft Health Inference Engine (SHINE) is a software-development tool for knowledge-based systems and has been created as a product for research and development by the Artificial intelligence Group, Information Systems Technology Section at NASA/JPL to meet many of their demanding and rigorous AI goals for current and future needs. The system is now in regular use in basic and applied AI research at JPL. SHINE was developed as a system that was designed to be efficient enough to operate in a real-time environment and to be utilized by non-LISP applications written in conventional programming languages such as C and C++. These non-LISP applications can be running in a distributed computing environment on remote computers or on a computer that supports multiple programming languages. It provides a variety of facilities for the development of software modules for the primary functions in knowledge-based reasoning engines. The system may be used to develop artificial intelligence applications as well as specialized tools for research efforts.

Knowledge-based systems for automated task planning, monitoring, diagnosis and other applications require a variety of software modules based on artificial intelligence concepts and advanced programming techniques. The design and implementation of the modules require considerable programming talent and time and background in theoretical artificial intelligence. Sophisticated software development tools that can speed the research and development of new artificial intelligence applications are highly desirable. The SHINE system was developed for that purpose. Included in the system are facilities for developing reasoning processes, memory-data structures and knowledge bases, blackboard systems and spontaneous computation daemons. Computational efficiency and high performance are especially critical in artificial intelligence software.

SHINE is an optimizing compiler-based system. When an application is developed using SHINE, it is first translated into Common LISP code and then passed through an extensive optimizer. SHINE generates tailored code for each application. There are no intermediate levels of interpretation for execution unlike many commercial systems. SHINE programs are executed directly by the LISP interpreter and compiled directly by the LISP compiler. This means much greater speed and better portability to other machines. SHINE is a set of high level and low level software tools designed to assist in building stand-alone knowledge-based system applications, shells and tools.SHINE comes with libraries that implement most common problem solving techniques and representations. This means that you can make use of classical AI solutions that have been extensively used and tested by other users. These libraries can also be extended by your own problem solving techniques and representations. SHINE facilities are invoked directly by a programmer in the Common Lisp language. For improved efficiency, an optimizing compiler is included that generates highly optimized Common LISP code. SHINE allows embedded software written in other programming languages such as C, C++, and also permits software developed with the system to be part of larger, non-Common LISP applications.

Background[edit]

The original inventors of SHINE are Mark L. James and David J. Atkinson. SHINE is a high-speed expert system and inference engine based upon the experience, requirements and technology that were collected over the years by the Artificial Intelligence Research group at NASA/JPL in developing expert systems for the diagnosis of spacecraft health.[1] SHINE is based on technology first developed by James and Atkinson for the "STAR*TOOL" system.[2] SHINE itself resulted from applying this technology in a project called "Spacecraft Health Automated Reasoning Pilot" (SHARP). SHARP aimed to automate and provide expert system consultation to space flight operations personnel who monitor and diagnosis robotic spacecraft on science missions, such as the Voyager spacecraft.[3][4]

Knowledge acquisition and implementation from experts is an inefficient and painful process for most automation implementation projects. The phase is often so wrought with difficulty, that the success of the automation project as a whole is jeopardized. The following describes a system called Spacecraft Health Inference Engine (SHINE) which provides a number of solutions to this problem. SHINE is a state-of-the-art solution for Artificial Intelligence (AI) and non-AI problems that up to this point were either impossible or impractical to solve.

It is intended for those areas of inferencing where speed, portability and reuse is of critical importance. Such areas would include spacecraft monitoring, control and health, telecommunication analysis, medical analysis, financial and stock market analysis, fraud detection (e.g. banking and credit cards), robotics or basically any area where rapid and immediate response to high-speed and rapidly changing data is required.

SHINE was independently evaluated by UC Berkeley and was shown to significantly outperform commercially available inference engines such as RTI and ART. It executes approximately 500,000,000 plus rules a second running on a standard Windows PC.

  • SHINE is written in Common LISP and can be easily run on any system that supports the language. It has been successfully interfaced to many non-LISP systems without any problems.
  • Beyond Limits has the Caltech licensing rights to all commercial applications of SHINE.[5] They are currently working on both product and commercial enhancements to the SHINE technology as well as several Expert System applications in healthcare, energy, telco, finance, manufacturing and other IoT markets.

SHINE has been used in the following NASA and non-NASA applications[edit]

  • Spacecraft Health Automatic Reasoning Pilot (SHARP) for the diagnosis of telecommunication anomalies during the Neptune Voyager (VGR) Encounter.[6]
  • Galileo (GLL) mission for diagnosing problems in the Power and Pyro Subsystem (PPS).
  • Magellan (MGN) mission for diagnosis of telecommunication anomalies in the TELECOM subsystem.
  • Engineering Analysis Subsystem Environment (EASE) which is an operations environment to operate a large number of spacecraft simultaneously, maintain high reliability levels and increase productivity through shared resources and automation.
  • Extreme UltraViolet Explorer (EUVE) mission for labor 3 to 1 shift reductions through the use of artificial intelligence.
  • Fault Induced Document Officer (FIDO) for the EUVE mission. which is an automated system that assists in expert knowledge acquisition, access and publishing capabilities for safely managing complex systems under staffing reductions and "lights out" operations.
  • Stochastic Problem Obviation Tracker (SPOT) for the EUVE mission which captures and reports relevant statistical information to the user based on operations within the FIDO environment.
  • Under consideration by a medical company for real-time diagnosis of rectal colon cancer.
  • Under consideration by a medical company for an expert system for the control of the robotic systems used in eye surgery.

External links[edit]

References[edit]

  1. ^ Atkinson, D.J., "Artificial intelligence for monitoring and diagnosis of robotic spacecraft." Doctoral Dissertation. School of Electrical and Computer Engineering, Chalmers University of Technology, Göteborg, Sweden, ISSN 0282-5406; no 237. ISBN 91-7032-755-6. 1992.
  2. ^ James, Mark and Atkinson, David, "STAR*TOOL - An Environment and Language for Expert System Implementation",JPL Report NTR C-1736, Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States. August 19, 1988.
  3. ^ Atkinson, D.J., Doyle, R.J, James, M.L., Kaufman, T., Martin, R.G., "Spacecraft Health Automated Reasoning Prototype (SHARP): The fiscal year 1989 SHARP portability evaluations task for NASA Solar System Exploration Division's Voyager project." NASA Technical Report, Number NASA-CR-187338, JPL-PUBL-90-20, Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States. August 18, 1990.
  4. ^ Atkinson, D.J., James, M.L., Lawson, D. Martin, R.G. and Porta, H. "Automated spacecraft monitoring". IEEE International Conference on Systems, Man and Cybernetics. IEEE: Los Angeles, CA. 4-7 Nov 1990. DOI: 10.1109/ICSMC.1990.142222 pp. 756 – 761
  5. ^ Press Release, VIASPACE. Retrieved 15 August 2014. VIASPACE Announces Licensing of World's Fastest Inferencing Engine - SHINE - From Caltech
  6. ^ Martin, R.G., Atkinson, D.J., James, M.L, Lawson, D.L. and Porta, H.J., "A report on SHARP (Spacecraft Health Automated Reasoning Prototype) and the Voyager Neptune encounter." NASA Technical Report, Number NASA-CR-187810, NAS 1.26:187810, JPL-PUBL-90-21, Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States. August 15, 1990.