Intelligent Systems Software Assurance Symposium 2004

1. Intelligent SystemsSoftware Assurance Symposium 2004 Bojan Cukic & Yan Liu, Robyn Lutz & Stacy Nelson, Chris Rouff, Johann Schumann, Margaret Smith July 22, 2004

2. “What” • Intelligent Systems research will create “new generations of robust, fault-tolerant software for intelligent, cooperative space systems that operate largely autonomously from ground control” --NASA list of key technology areas for H & RT Advanced Space Technology, 6/04 • New technologies for V&V of Intelligent Systems

3. “What” (cont.) • Technologies demonstrated at this year’s presentations: • Neural Networks • AI Planners • Support Vector Data Description algorithms • Bayesian-based safety envelopes • Autonomous contingency identification and recovery technology • Model Checking • Hybrid formal methods

4. Information Systems Presentations

5. Information Systems Presentations

6. Intelligent Systems: Why ? • Long lived missions • Lower operations costs • Swarms & constellations of satellites/spacecraft • Currently used in other domains: • automotive • health • waste water management • Intelligent Systems are here to stay!

7. Intelligent Systems: Why not • Is the technology: • Scalable for usage? • Being oversold? • Just a piece of a larger puzzle? • V&V of Intelligent Systems requires a new knowledge set: math, tools, control theory, and highly skilled software engineers. • V&V is scrambling to catch up to new technologies for Intelligent Systems

8. Directions? • Do we know yet how to design intelligent systems for verifiability? (or meaningless to lump them?) • Is the IV&V process different for intelligent systems? • Are we ready to demonstrate scalability on real systems? • Should we be developing V&V standards for intelligent systems? Tied to criticality levels? • How do we start establishing benchmarks for intelligent systems?

9. Verification and Validation of Adaptive Systems by Bojan Cukic • Investigate the role of modern AI techniques (Support Vector Machines) in failure detection and identification. • Failure Detection • Designing a fast (real-time) SVDD algorithm to detect failure conditions • Failure Identification • Failures are identified by studying the correlation between certain longitudinal and lateral dynamics parameters • Validate the technology in extensive simulations

10. Bayesian Verification and Validation tools for Adaptive Systems by Johann Schumann • Problems with traditional V&V methods applied to Adaptive Systems: • Fault avoidance design testing applies to base case only • Unanticipated failures? • Unmodeled failures? • Fault removal cannot test all possible configurations in advance • Fault tolerant design does not consider all possible problems

11. Bayesian Verification and Validation tools for Adaptive Systems by Johann Schumann • Methods for improvement: • Improve performance estimation of the neural network (Bayesian approach) • Use Envelope tool to answer: • How large is the current safe envelope? • How far is the operational point from the edge?

12. Formal Approaches to Swarm Technologies by Chris Rouff • Survey formal approaches for agent-based, multi-agent and swarm-based systems for appropriate swarm-based methods • Apply most promising approaches to parts of ANTS • Evaluate methods for needed properties • Model and outline swarm-based formal method • Develop formal method for swarm-based systems • Do formal specification of ANTS using new method • Prototype support tools

13. Formal Approaches to Swarm Technologies An ANTS Overview - by Chris Rouff

14. Contingency Software in Autonomous Systems by Robyn Lutz & Stacy Nelson • The Goal - Mitigate failures via software contingencies resulting in safer, more reliable autonomous vehicles in space and in FAA national airspace • How? • Adding intelligent diagnostic capabilities by supporting incremental autonomy • Responding to anomalous situations currently beyond the scope of the nominal fault protection • Contingency planning using the SAFE (Software Adjusts Failed Equipment) method

15. Model Checking of Artificial Intelligence Based Plannersby Margaret Smith • Goal: Using model checking, and specifically the SPIN model checker, retire a significant class of risks associated with the use of Artificial Intelligence (AI) Planners on Missions • Must provide tangible testing results to a mission using AI technology. • Should be possible to leverage the technique and tools throughout NASA. • FY04 Activities: • Identify and select candidate risks • Develop and demonstrate technique for testing AI Planners/artifacts on: • A toy problem (imaging/downlinking) – demonstrate tangible results with an abstracted clock/timeline • A real problem (DS4/ST4 Champollion Mission) – demonstrate, using DS4 AI input models, that Spin can determine if an AI input model permits the AI planner to select ‘bad plans’.

16. Lyapunov Stability Analysis and On-Line Monitoring by Bojan Cukic • The Problem: • Issues with Adaptive Systems: uncertainty/newness • Need Understanding of self stabilization analysis techniques suitable for adaptive system verification • Need to investigate effective means to determine the stability and convergence properties of the learner in real-time • The Approach: • Online Monitoring • Confidence Evaluation

17. Lyapunov Stability Analysis and On-Line Monitoring by Bojan Cukic • Relevance to NASA: • Artificial Neural Networks are increasingly important in flight control and navigation • Autonomy and adaptability are important features in many NASA projects • The theory is applicable to future agent-based applications