An Assessment of Artificial Intelligence Technologies for Vehicle Management Systems

1. An Assessment of Artificial Intelligence Technologies for Vehicle Management Systems Kamara Brown 1, Dr. Mike Watson 2, Dr. Luis Trevino 3 Engineering Directorate Spacecraft and Vehicles Systems Department Advanced Sensors and Health Management Systems Branch (EV23) 1 Research Associate, The George Washington University 2 Principal Investigator, Branch Chief, Advanced Sensors & Health Management Systems 3 Principal Investigator, Artificial Intelligence Researcher, Advanced Sensors & Health Management Systems

2. Presentation Agenda • Introduction • Research Objective • Research Problem Statement • Research Methodology (Contributions) • Data Analysis • Results

3. Introduction • Future of Transportation • Defining Artificial Intelligence (AI)

4. Future of Transportation: Enabler for Growth Throughout history, transportation has been the key to economic progress and discovery. Space Transportation continues to be the next logical step.

5. Artificial Intelligence (AI) • Strong relation to intelligence • Controversial • What is intelligence? • Characteristics of AI • Efficiently solve problems of reasoning and decision making under uncertainty • Acquire and extract knowledge from data, experience, and experts • Adjust the behavior to changes in the surrounding environment and efficiently respond to new situations • AI systems are meant to solve problems or assist people in solving problems of decision-making under uncertainty using explicit representation of knowledge and reasoning methods employing that knowledge

6. Research Objective • To spearhead a study on how AI Techniques can create intelligent (decision-making) space vehicle systems • To analyze and determine the most prominent techniques

7. Research Problem Statement As NASA moves towards planning deep space missions, there is a need for examining applications utilizing autonomous systems and AI technologies. This will allow space vehicle systems that can make decisions on its on. The intelligent system must dynamically select the “optimum” configurations for supporting such critical subsystems like crew environment, electrical power systems, propulsion systems.

8. Research Methodology Methodology included my PIs and myself • Conducting In-depth informational interviews with leading researchers in their field • Questions: • Current automated / autonomous methods utilized • Advantages & challenges of these methods • Parameters and values necessary • Performing a comprehensive literature review – • Searched technical databases, professional journals, & conference proceedings • Selected the top 3 AI methods • Reanalyzed these methods – scoped it down to One Main

9. Presentation Agenda • Introduction • Research Objective • Research Problem Statement • Research Methodology (Contributions) • Data Analysis • Results

10. Data Analysis Leading 3 Artificial Intelligence Methods • Bayesian Belief Networks • Neural Networks • Fuzzy Logic • Utilized the most • Industry • Existing technologies.

11. Data Analysis • Composed of group of connected neurons • Often grows into massive interconnection problem • Powerful in modeling cause & • effects in wide variety of • domains • Compact networks of • probabilities • Effective automated tuning • mechanism • Extension of Boolean logic • Can merge with other • approaches (i.e. neural • networks, statistics) • Problems in design & • implementation in higher • dimensional applications http://smig.usgs.gov/SMIG/features_0902/tualatin_ann.fig3.gif

12. Foundation of Bayesian Belief Network Bayes Theorem The probability of b given a equals the probability of a given btimes the probability b, divided by the probability of a.

13. Conceptual Bayesian Network Design for Autonomous, Rendezvous, & Docking (AR&D) Systems Conceptual Bayesian Network Design for AR&D Systems AI = Actual Image GI = Ghost Image Distance Between AI & GI Width - AI Height - AI Height - GI Width - GI AI GI Belief True Target

14. Results • Bayesian Belief Network • Appears to be the most prominent for space applications • Why? • Allows flexible • Does not need any previous knowledge (very user friendly) • Graphical representation with strong mathematical foundation

15. Results • Bayesian Belief Networks (BBN) • BBNs are not all-in-encompassing” • Need combined probability algorithms for super decision-making • systems • Recommendations: - Hybrid AI Approach to model for AR&D system • - Further investigation on the development of • smart systems for future space missions (Low Earth Orbit) • - Additional studies and testing of modeling • this technique

16. Acknowledgments NASA Marshall Space Flight Center Dr. Mike Watson, Dr. Luis Trevino Dr. Katherine Chavis John Wiley, Amanda Duffell, Valentin Korman Dr. Deidre Paris Linda Brewster Ricky Howard Special Thanks to: Dr. Frank Six Dr. Gerry Karr Dr. Ruth Jones Jessica Culler NASA Academy Staff & 2005 NASA Academy Research Associates

17. References Cook, W. Sidney, and Scott D. Lindell, “Autonomous Rendezvous and Docking (AR&D) for Future Spacecraft Missions,” AIAA paper 99-4598 presented at the AIAA Space Technology Conference & Exposition, Sept. 28-30, 1999, Albuquerque, NM. Judea Pearl, “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Interface”, pages 1-36, 143-174, The Morgan Kaufmann Series in Representation and Reasoning, Morgan Kaufmann Publishers, Inc, San Mateo, California B.J.A. Krose, N. Vlassis, and W. Zajdel. Bayesian methods for tracking and localization. In Proc. Of Philips Symposium On Intelligent Algorithms, (SOIA), pages 27-38, 2004 (Gzipped PostScript, 12 pages, 184 Kbytes) (PDF, 167 Kbytes)